{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AMD-Testing.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "69OA5Nw4K6lQ",
        "GxelWq612yjX",
        "wCf2x1W9oJrW",
        "Vaa4Ch-GpiiM",
        "7d0XlOLtzsQi"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSiyOMYx1JY-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import librosa\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RafkM3Vs1Vfs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VG01KwHezoC",
        "colab_type": "text"
      },
      "source": [
        "# Building Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQTVTkJp154e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def process(file, label):\n",
        "  try:\n",
        "    print(\"load file {}\".format(file))\n",
        "    # here kaiser_fast is a technique used for faster extraction\n",
        "    \n",
        "    X, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "    X = librosa.resample(X, sample_rate, 16000)\n",
        "      \n",
        "    duration = librosa.get_duration(X, sample_rate)\n",
        "    stft = np.abs(librosa.stft(X))\n",
        "    mfccs_40 = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
        "    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)    \n",
        "    mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate,n_mels=128,fmax=8000).T,axis=0)\n",
        "    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),\n",
        "    sr=sample_rate).T,axis=0)\n",
        "    row = pd.Series({'file_path':file,'mfccs_40':mfccs_40, 'chroma':chroma, 'mel':mel, 'contrast':contrast, 'tonnetz':tonnetz, 'duration':duration,'label':label},name=7)\n",
        "    return row\n",
        "  \n",
        "  except Exception as e:\n",
        "    print(\"Error encountered while parsing file: \", file)\n",
        "    \n",
        "def download_files(path, label, max=150):\n",
        "  index = 0\n",
        "  for file in glob.glob(path):\n",
        "    if index <=max:\n",
        "      row = process(file, label)\n",
        "      df.loc[len(df)] = row\n",
        "      index = index +1\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIM7jKNa1Yzz",
        "colab_type": "code",
        "outputId": "9e592544-25f6-4de8-c4a2-08957a949cbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6463
        }
      },
      "source": [
        "#compute\n",
        "import glob, os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df = pd.DataFrame(columns=['file_path', 'mfccs_40', 'chroma', 'mel','contrast','tonnetz', 'duration','label'])\n",
        "df = df.fillna(0) # with 0s rather than NaNs\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/answering-machine/*.wav\", label=\"beep\")\n",
        "download_files(\"drive/My Drive/amd-training/careangel-recordings/*.wav\", label=\"beep\")\n",
        "download_files(\"drive/My Drive/amd-training/soundbible_beep_sounds/*.mp3\", label=\"beep\")\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/speech-recordings/*.wav\", label=\"speech\")\n",
        "download_files(\"drive/My Drive/amd-training/answeringmachinemessages/*.mp3\", label=\"speech\")\n",
        "\n",
        "df = df.drop_duplicates(subset=['file_path'], keep=False)\n",
        "df.dropna(inplace=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/answering-machine/8cc2e697-18ac-490d-b111-c90350708684.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e5030474-e71d-42ef-ae19-9de293a60c05.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/55b654e5-7d9f-4132-bc98-93e576b2d665.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/77310681-9ccf-4da7-bf55-dda0815dcf04-2.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/cbbc6d2a-5bf3-4ca4-9920-a12ad6d75d9e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e54d1f7e-d1c9-4982-80e9-eeb56e714840.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/359198a9-a8f1-44b6-a4f4-8383c85fc49d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/abe96bb3-4304-402e-a88b-7de453c31ce1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a210698c-5e60-48bd-9ba3-c00981b0427a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b70c30b0-a1ca-43b5-b9cb-60b1a049464f.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ea19ab7e-ad09-4736-8e68-f1fc06ca629f.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f0c3a2ca-4a89-4b0f-a20f-dccf6f756a0c.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/24c0d8e4-496f-4f8b-b8a3-2a0b61f0ce72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e19685f0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/c2823a7a-87f9-4689-9b53-d37f76964243.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/97a64f29-34c6-4638-936b-fb788f1c9011.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/38c5b9ca-9644-4672-bd14-d5ad0983f528.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/6d0c8d00-0202-11e9-bb68-51880c8718e4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e7b49010-97b4-4a03-af7b-5ee4606a4311.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/d8d9c220-02fa-11e9-96e0-fb0b86aa2d7e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b11ab910-02dc-11e9-8fd9-1d874d96e575.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/75ea624b-8ab9-4e17-9000-70e96166642a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a54eb99a-3bb3-456f-bed8-90e929f0cc90.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f78dbcc0-02dd-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/3dbc0174-3084-4641-92ac-db7ae40b3c20.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/d04d669f-6107-405b-8b82-b5327cf34cec.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/72642a94-b9a6-4084-b696-af9e37bd0b01.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/174add6a-1bca-4724-9524-3afb55a4966d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f69e1982-0acf-4281-95a1-b5151aa3c2ad.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/97d83725-4626-4d99-b691-a3d67d3f6ced.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/370e97a8-f253-4b47-a647-9a103c0618ef.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/280215f0-02f1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8c453cd4-eea0-47e7-8489-2fa34197868b-old1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/167f2373-d222-4089-893b-76d4d2a1116a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/df101063-0935-4a03-be60-ea495a48fc3b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/07a3d677-0fdd-4155-a804-37679c039a8e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a2d34274-72a9-47f5-af9a-2f9bb4f8145f.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/0fc18be5-72e7-48c9-a17c-7aa3af659c0c.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8aa7d5ff-b936-49c2-a1d5-bcfa73587bcd-old1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a7e954c0-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/77310681-9ccf-4da7-bf55-dda0815dcf04.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/96267e1a-b077-4fa7-bb1a-e31bb73ef2ae.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2bbc5b10-7c47-4747-a5c6-b4aabb845d8b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/0c9d15d7-876f-4915-896e-2cbd8547f569.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/470d36a0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/441d0f84-2685-4c40-acc7-37f08b251df5.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/24ef7595-0cd8-4637-b59f-276114b07a4e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2a685eda-8dd9-4a4d-b00e-4f43715f81a4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e11b5295-fe30-47c0-a9a8-309c954891cc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f7b071ce-c6d9-4973-a4ac-ffc1996d6141.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b-2.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b5ff3b00-02e5-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/699603ae-8f1d-4260-9b25-6132c5c6fe3a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/7eaeb600-0202-11e9-bb68-51880c8718e4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/1f8d4133-83e7-4afa-a7be-3233993761aa.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8917c4b3-51fa-4456-9384-65678f6e19f9.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-e149b51a4e1d4bb7b67f2f05c072dcb5-20190109T145619.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/41d929b9-59d2-4634-bdea-7fd7f4f18b05.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-c27052dcc9544aeba68712d93fd3f238-20190424T152320.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/1e194459-a1f1-4320-b580-d7cd7f89e5fc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/fbae314e-6ef2-4cd7-ae2a-725ddd43c890.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/6677c6bf-1e9f-4e8d-a1ca-7fec9a091702.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b728940b-dd5b-4764-bf7a-039399759795.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/41d929b9-59d2-4634-bdea-7fd7f4f18b05 (1).wav\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
            "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/answering-machine/1b1d6385-7a20-4c3b-9759-b216cb358c51 (1).wav\n",
            "load file drive/My Drive/amd-training/answering-machine/4d668e28-28d9-480a-9075-f0b4069a7020.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ec0dfded-d54e-4c96-838a-8c2b64157469-1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ec0dfded-d54e-4c96-838a-8c2b64157469.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-8587b299448c48989063c49ee5cf9d59-20190503T080106.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-fa42988185744bb0a675ec0049d122d5-20190503T080614.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-7f3a67f622bd47ac902dfbf4a3a89377-20190503T125031.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131537.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-39427f912abe42d29d4fb65312c8697e-20190503T125506.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T185259.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T185559.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T193729.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T184915.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T185600.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T185300.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T193730.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184919.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184919.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-bd3faaf86cc74dfab89b73a0ec6531de-20190123T124551.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184632.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T185258.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184410.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184634.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T123507.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T183610.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T193725.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T185602.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184635.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T185302.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T124552.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184407.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184917.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T193727.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T185302.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-07006f1917444b48a69b30fc128cd0e6-20190123T193728.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T124549.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T124849.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T123507.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T185223.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184329.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T123218.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T185226.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184842.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184559.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184600.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T183536.wav\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/sos-morse-code_daniel-simion.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/A-Tone-His_Self-1266414414.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Annoying_Alien_Buzzer-Kevan-1659966176.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Censored_Beep-Mastercard-569981218.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Door_entry_notification-Loge_the_60th-95203129.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Modem-KP-551027942.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Robot_blip-Marianne_Gagnon-120342607.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Robot_blip_2-Marianne_Gagnon-299056732.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/House Fire Alarm-SoundBible.com-1609046789.mp3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
            "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Electronic_Chime-KevanGC-495939803.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Computer Error Alert-SoundBible.com-783113881.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Eas Beep-SoundBible.com-238025417.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Bleep-SoundBible.com-1927126940.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Beep-SoundBible.com-923660219.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Morse Code-SoundBible.com-810471357.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Beep Ping-SoundBible.com-217088958.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Busy Signal-SoundBible.com-1695161320.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Radio Interruption-SoundBible.com-1434341263.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Truck Air Brakes-SoundBible.com-1928162780.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Trash Truck Reverse Beep-SoundBible.com-1645900246.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Horn Honk-SoundBible.com-1162546405.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/TV Off Air-SoundBible.com-2131365426.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Weather Alert-SoundBible.com-2072200951.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Strange Noise-SoundBible.com-229408508.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Music Censor-SoundBible.com-818434396.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Heart Rate Monitor Flatline-SoundBible.com-2063567528.mp3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
            "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Censor Beep-SoundBible.com-250233510.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Grocery Scanning-SoundBible.com-1637675042.mp3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
            "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Checkout Scanner Beep-SoundBible.com-593325210.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Microwave Alarm-SoundBible.com-382933696.mp3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
            "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Beep 2-SoundBible.com-1798581971.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Beep-SoundBible.com-1689177436.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Backing Up-SoundBible.com-788937884.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Horn Honk-SoundBible.com-1634776698.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Strange Beeping-SoundBible.com-2088039238.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Alarm Clock-SoundBible.com-437257341.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Pager Beeps-SoundBible.com-260751720.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Fuzzy Beep-SoundBible.com-1580329899.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Radar Detector Beeps-SoundBible.com-892148482.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Short Beep Tone-SoundBible.com-1937840853.mp3\n",
            "load file drive/My Drive/amd-training/soundbible_beep_sounds/Answering Machine Beep-SoundBible.com-1804176620.mp3\n",
            "load file drive/My Drive/amd-training/speech-recordings/aa0b4100-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05sdfs.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e3fa7fe0-02e2-11e9-aa3d-ad1a095d8d72asdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05asdsa.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e11b5295-fe30-47c0-a9a8-309c954891ccasdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/d8d9c220-02fa-11e9-96e0-fb0b86aa2d7easdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e54d1f7e-d1c9-4982-80e9-eeb56e714840asdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/b5ff3b00-02e5-11e9-aa3d-ad1a095d8d72sdfsd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/b11ab910-02dc-11e9-8fd9-1d874d96e575dsfsdf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e19685f0-02e7-11e9-aa3d-ad1a095d8d72sdfsdf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5f2-de62afc471bfsdfsd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5dddf2-de62afc471bf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f457d760-02e3-11e9-aa3d-ad1a095d8d72dfds.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f47cb7ab-4572-4f96-84ad-cda3c3a333c2.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/acdsf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f0c3a2ca-4a89-4b0f-a20f-dccf6f756a0c.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f457d760-02e3-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629fasdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a210698c-5e60-48bd-9ba3-c00981b0427a.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/abe96bb3-4304-402e-a88b-7de453c31ce1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e7b49010-97b4-4a03-af7b-5ee4606a4311sd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e7b49010-97b4-4a03-af7b-5ee4606a4311.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/df101063-0935-4a03-be60-ea495a48fc3b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f7b071ce-c6d9-4973-a4ac-ffc1996d6141.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759e.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759ds.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5f2-de62afc471bf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f7b071ce-c6d9-4973-a4ac-ffc1996d6141w.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e54d1f7e-d1c9-4982-80e9-eeb56e714840.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/49298a15-27b4-4df2-bee4-2faf0a9191f8.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/96267e1a-b077-4fa7-bb1a-e31bb73ef2ae.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/472e7940-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/72642a94-b9a6-4084-b696-af9e37bd0b01.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/81270a2a-088b-4e3c-9f47-fd927a90b0abs.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/280215f0-02f1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/538549a3-18e8-40a0-9462-8f20cb1cee11.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/75ea624b-8ab9-4e17-9000-70e96166642a.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/77310681-9ccf-4da7-bf55-dda0815dcf04d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/77310681-9ccf-4da7-bf55-dda0815dcf04.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a2d34274-72a9-47f5-af9a-2f9bb4f8145fe.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a2d34274-72a9-47f5-af9a-2f9bb4f8145f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011SD.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/174add6a-1bca-4724-9524-3afb55a4966d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/252aa640-02e1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/110ac98e-34fa-42e7-bbc5-450c72851db5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/232b3f2d-8c3a-44b2-97ec-915a5f3b6cf1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011W.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/370e97a8-f253-4b47-a647-9a103c0618ef.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011D.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/470d36a0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/55b654e5-7d9f-4132-bc98-93e576b2d665.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/33ce1bf0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/470d36a0-02e7-11e9-aa3d-ad1a095d8d72C.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/52a31be4-c4b1-46bc-9aa5-cb0090183bb6.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/441d0f84-2685-4c40-acc7-37f08b251df5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/38c5b9ca-9644-4672-bd14-d5ad0983f528.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/55b654e5-7d9f-4132-bc98-93e576b2d665d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8cc2e697-18ac-490d-b111-c90350708684.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07dad.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903ded.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9e.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bccd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07j.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6XN9trtsQGo-old1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99efe-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1eb92c03-b085-4872-9b70-cb725cf7119d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/0c9d15d7-876f-4915-896e-2cbd8547f569.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4de.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120836.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830 (1).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120842.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825 (1).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc (2).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-3.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080101.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080058.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080210.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080225.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080241.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131546.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125458.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131547.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125504.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125501.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125505.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125459.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-bacbcd61fa404c699a3ddc1bf4f1202c-20190503T143916.wav\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AdamSandlerPotSmokers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Africain.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AlzheimerAnsweringMachine.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AnsweringService.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AquaTeenHungerForce-Meatwad.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ArnoldSchwarzeneggerPizzaDelivery.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AustinPowers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BipLeFlic.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BobAndTom.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BritneySpears.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CantFindPhone.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CheechAndChong.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Chinois.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CledusTJudd-PotSmokers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Coca-Cola.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Confused.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CradleOfFilth-Danis.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DaDaDa.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DaneCook.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DialAnAsshole.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DonnieBaker.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ElvisPresley.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Eminem.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FBIvsTaliban.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FamilyGuy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Farts.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Flintstones.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FriteSurLaLigne.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Garfield-NakedJello.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-GeorgeCarlin.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Hell.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-IndianGuyRingTone.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Irish.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JacquesChirac.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JerkyBoys.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JohnnyHalliday.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-KittyWillDie.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LearnJapanese.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LeaveAMotherFuckingMessage-TheJerkyBoys.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Les7Nains.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LorenaBobbit.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LouisDeFunes.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MachinesHomer.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MeetTheFockers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MissionImpossible.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-ImBusy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-Insult.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-SillyVoices.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MultipleChoice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PMSHotline.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Paysan.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PorkyPig.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Prozac.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PsychiatricOffice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PulpFiction.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-RenAndStimpy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ScoobyDoo.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SecretMission.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SeinfeldGeorge.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SexStory.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ShirleyQLiquor.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SilenceOfTheLambs.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SmithWessonAndMe.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SpiceGirls.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-StarTrek.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Suicide.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Taliban.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Tarzan.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TelephoneWarning.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TheSimpsons-SpringfieldPolice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TheYouthAhead.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-UnVoleur.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-UnitedStatesMarineCorps.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-WandaSykes.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-WonACar.mp3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BC0p9wGD3W6A",
        "colab_type": "code",
        "outputId": "1a7cfb89-7672-44df-846c-11cace651f2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 368 entries, 0 to 367\n",
            "Data columns (total 8 columns):\n",
            "file_path    368 non-null object\n",
            "mfccs_40     368 non-null object\n",
            "chroma       368 non-null object\n",
            "mel          368 non-null object\n",
            "contrast     368 non-null object\n",
            "tonnetz      368 non-null object\n",
            "duration     368 non-null float64\n",
            "label        368 non-null object\n",
            "dtypes: float64(1), object(7)\n",
            "memory usage: 25.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgW4Zs5nfZJz",
        "colab_type": "text"
      },
      "source": [
        "# DF Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjlEKLOLff9Q",
        "colab_type": "code",
        "outputId": "05154c52-d43e-4a1e-bebe-9f54f31b993d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file_path</th>\n",
              "      <th>mfccs_40</th>\n",
              "      <th>chroma</th>\n",
              "      <th>mel</th>\n",
              "      <th>contrast</th>\n",
              "      <th>tonnetz</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>drive/My Drive/amd-training/answering-machine/...</td>\n",
              "      <td>[-379.78920197198744, 109.52652158523611, -63....</td>\n",
              "      <td>[0.27032481878700204, 0.05348226776316049, 0.0...</td>\n",
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              "      <td>0.227528</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>drive/My Drive/amd-training/answering-machine/...</td>\n",
              "      <td>[-503.14043618726924, 97.91657901160586, -88.7...</td>\n",
              "      <td>[0.04605066635072472, 0.23692643368296615, 1.0...</td>\n",
              "      <td>[7.806247901451935e-05, 2.3955645604629927e-05...</td>\n",
              "      <td>[17.19046710455086, 12.898032765815296, 13.740...</td>\n",
              "      <td>[0.04201544101511034, 0.027156067648276636, 0....</td>\n",
              "      <td>0.284172</td>\n",
              "      <td>beep</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>drive/My Drive/amd-training/answering-machine/...</td>\n",
              "      <td>[-493.4307644356875, 99.41443683761632, -85.67...</td>\n",
              "      <td>[0.05874194507054588, 0.23422284828411624, 1.0...</td>\n",
              "      <td>[0.00014522694641017108, 0.0001266829324090013...</td>\n",
              "      <td>[18.060194986066143, 11.580904339789774, 12.10...</td>\n",
              "      <td>[0.05363053816254126, 0.009149971251823444, -0...</td>\n",
              "      <td>0.284989</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drive/My Drive/amd-training/answering-machine/...</td>\n",
              "      <td>[-260.5614328799362, 94.07030790222211, -120.4...</td>\n",
              "      <td>[0.2623837806852157, 1.0, 0.2666184448982902, ...</td>\n",
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              "      <td>[12.5846937008122, 9.913521267798984, 11.24649...</td>\n",
              "      <td>[-0.011873148568373328, 0.007187880250277481, ...</td>\n",
              "      <td>0.121633</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drive/My Drive/amd-training/answering-machine/...</td>\n",
              "      <td>[-432.81462243071866, 123.00607628658472, -66....</td>\n",
              "      <td>[0.23865130126866835, 0.05035802591554152, 0.0...</td>\n",
              "      <td>[6.847455416667602e-05, 0.00011353229587751057...</td>\n",
              "      <td>[12.118921583096693, 10.282943235291603, 13.13...</td>\n",
              "      <td>[0.030742900690135584, -0.03863135972705621, 0...</td>\n",
              "      <td>0.228889</td>\n",
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            ],
            "text/plain": [
              "                                           file_path  \\\n",
              "0  drive/My Drive/amd-training/answering-machine/...   \n",
              "1  drive/My Drive/amd-training/answering-machine/...   \n",
              "2  drive/My Drive/amd-training/answering-machine/...   \n",
              "3  drive/My Drive/amd-training/answering-machine/...   \n",
              "4  drive/My Drive/amd-training/answering-machine/...   \n",
              "\n",
              "                                            mfccs_40  \\\n",
              "0  [-379.78920197198744, 109.52652158523611, -63....   \n",
              "1  [-503.14043618726924, 97.91657901160586, -88.7...   \n",
              "2  [-493.4307644356875, 99.41443683761632, -85.67...   \n",
              "3  [-260.5614328799362, 94.07030790222211, -120.4...   \n",
              "4  [-432.81462243071866, 123.00607628658472, -66....   \n",
              "\n",
              "                                              chroma  \\\n",
              "0  [0.27032481878700204, 0.05348226776316049, 0.0...   \n",
              "1  [0.04605066635072472, 0.23692643368296615, 1.0...   \n",
              "2  [0.05874194507054588, 0.23422284828411624, 1.0...   \n",
              "3  [0.2623837806852157, 1.0, 0.2666184448982902, ...   \n",
              "4  [0.23865130126866835, 0.05035802591554152, 0.0...   \n",
              "\n",
              "                                                 mel  \\\n",
              "0  [0.005562816976167255, 0.0003082504894551795, ...   \n",
              "1  [7.806247901451935e-05, 2.3955645604629927e-05...   \n",
              "2  [0.00014522694641017108, 0.0001266829324090013...   \n",
              "3  [0.040916334065404784, 0.029089462654893983, 0...   \n",
              "4  [6.847455416667602e-05, 0.00011353229587751057...   \n",
              "\n",
              "                                            contrast  \\\n",
              "0  [17.815662982405097, 9.270500351294086, 14.366...   \n",
              "1  [17.19046710455086, 12.898032765815296, 13.740...   \n",
              "2  [18.060194986066143, 11.580904339789774, 12.10...   \n",
              "3  [12.5846937008122, 9.913521267798984, 11.24649...   \n",
              "4  [12.118921583096693, 10.282943235291603, 13.13...   \n",
              "\n",
              "                                             tonnetz  duration label  \n",
              "0  [0.016557670236403872, -0.044106911332447894, ...  0.227528  beep  \n",
              "1  [0.04201544101511034, 0.027156067648276636, 0....  0.284172  beep  \n",
              "2  [0.05363053816254126, 0.009149971251823444, -0...  0.284989  beep  \n",
              "3  [-0.011873148568373328, 0.007187880250277481, ...  0.121633  beep  \n",
              "4  [0.030742900690135584, -0.03863135972705621, 0...  0.228889  beep  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdezvgeK7r4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def one_hot_encode(df):\n",
        "  #convert label seriers to ints\n",
        "  from sklearn import preprocessing\n",
        "  le = preprocessing.LabelEncoder()\n",
        "  df['label'] = le.fit_transform(df['label'].astype(str))\n",
        "\n",
        "  print(df.label.value_counts())\n",
        "  le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
        "  print(le_name_mapping)\n",
        "  class_names = le.classes_\n",
        "  return df\n",
        "\n",
        "def remove_long_samples(df, duration=10):\n",
        "  df = df.copy()\n",
        "  df = df.drop(df[ df['duration']>duration].index)\n",
        "  df.reset_index(drop=True, inplace=True)\n",
        "  return df\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_X_njGQSmx8",
        "colab_type": "code",
        "outputId": "33456b3e-50dd-4c66-aadb-4316a4511672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df = one_hot_encode(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    196\n",
            "0    172\n",
            "Name: label, dtype: int64\n",
            "{'beep': 0, 'speech': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M546UokywWRT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = remove_long_samples(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X95yIMcUnDuL",
        "colab_type": "code",
        "outputId": "ffc94c8f-f134-40f8-9271-323d9ca1fc8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.label.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    168\n",
              "1    121\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UWVjMjAghSe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "import datetime\n",
        "\n",
        "def generateFeaturesLabels(features_list, df):\n",
        "  total_features_len = np.sum([len(df[feature][0]) for feature in features_list])\n",
        "  print(\"total number of features\",total_features_len)\n",
        "  features, labels = np.empty((0,total_features_len)), np.empty(0)\n",
        "  for index, row in df.iterrows():\n",
        "    a = []\n",
        "    for feature in features_list:\n",
        "      a.append(row[feature])\n",
        "      \n",
        "    features = np.vstack([features,np.hstack(a)])\n",
        "    labels = np.append(labels, row[\"label\"])\n",
        "  return np.array(features), np.array(labels, dtype = np.int)\n",
        "\n",
        "def score(model, X_test, y_test):\n",
        "  print(\"Score:\",model.score(X_test, y_test))\n",
        "\n",
        "  cross_val_scores = cross_val_score(model, X, y, cv=5, scoring='f1_macro')\n",
        "  print(\"cross_val_scores:\", cross_val_scores)\n",
        "  print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cross_val_scores.mean(), cross_val_scores.std() * 2))\n",
        "\n",
        "  predictions = model.predict(X_test)\n",
        "\n",
        "  print(metrics.accuracy_score(y_test, predictions))\n",
        "  print(metrics.classification_report(y_test, predictions))\n",
        "  print(metrics.confusion_matrix(y_test, predictions))\n",
        "  print(\"\")\n",
        "\n",
        "\n",
        "def save_model(model):\n",
        "  filename = \"{}-{}.pkl\".format(model.__class__.__name__,datetime.datetime.now().strftime(\"%Y%m%dT%H%M\"))\n",
        "  pickle.dump(model, open(filename, 'wb'))\n",
        "\n",
        "  from google.colab import files\n",
        "  files.download(filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v81dcHyzWdly",
        "colab_type": "text"
      },
      "source": [
        "## Training With only feature (mfccs_40)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JxTZgvZOrL2",
        "colab_type": "code",
        "outputId": "5a2955d6-c4b5-44e4-e8c7-43381b50d505",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#working model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "features = ['mfccs_40']\n",
        "X, y = generateFeaturesLabels(features, df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total number of features 40\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zl4C9uMYIFvx",
        "colab_type": "text"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RltOaSOfdXq",
        "colab_type": "text"
      },
      "source": [
        "## Train Classifiers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0vhsQBTQ7zj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code source: Gaël Varoquaux\n",
        "#              Andreas Müller\n",
        "# Modified for documentation by Jaques Grobler\n",
        "# License: BSD 3 clause\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.datasets import make_moons, make_circles, make_classification\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "\n",
        "def train_classifiers(X, y):\n",
        "  trained_classifiers = []\n",
        "  classifiers = [\n",
        "      KNeighborsClassifier(3),\n",
        "      SVC(kernel=\"linear\", C=0.025),\n",
        "      SVC(gamma=2, C=1),\n",
        "      GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
        "      DecisionTreeClassifier(max_depth=5),\n",
        "      RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
        "      MLPClassifier(alpha=1),\n",
        "      AdaBoostClassifier(),\n",
        "      GaussianNB(),\n",
        "      QuadraticDiscriminantAnalysis()]\n",
        "\n",
        "  names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
        "           \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
        "           \"Naive Bayes\", \"QDA\"]\n",
        "  i = 0\n",
        "  for classifier in classifiers:\n",
        "    print(\"***\"+ names[i] + \"*****\")\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "\n",
        "    fit_model = classifier.fit(X_train, y_train)\n",
        "    trained_classifiers.append(fit_model)\n",
        "    \n",
        "    print(\"Score:\",fit_model.score(X_test, y_test))\n",
        "\n",
        "    cross_val_scores = cross_val_score(fit_model, X, y, cv=5, scoring='f1_macro')\n",
        "    print(\"cross_val_scores:\", cross_val_scores)\n",
        "    print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cross_val_scores.mean(), cross_val_scores.std() * 2))\n",
        "\n",
        "    predictions = fit_model.predict(X_test)\n",
        "\n",
        "    print(metrics.accuracy_score(y_test, predictions))\n",
        "    print(metrics.classification_report(y_test, predictions))\n",
        "    print(metrics.confusion_matrix(y_test, predictions))\n",
        "    print(\"\")\n",
        "    i = i + 1\n",
        "  return trained_classifiers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80QUyLjghbx4",
        "colab_type": "code",
        "outputId": "fe76d22c-a0f5-4e9b-ff63-0c914f8cdc51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3301
        }
      },
      "source": [
        "X = list(df.mfccs_40.values)\n",
        "y = df.label.values\n",
        "classifiers = train_classifiers(X, y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***Nearest Neighbors*****\n",
            "Score: 0.9270833333333334\n",
            "cross_val_scores: [0.89588235 0.96484848 0.87898659 0.82860007 0.75248139]\n",
            "Accuracy: 0.86 (+/- 0.14)\n",
            "0.9270833333333334\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.90      0.94        58\n",
            "           1       0.86      0.97      0.91        38\n",
            "\n",
            "   micro avg       0.93      0.93      0.93        96\n",
            "   macro avg       0.92      0.94      0.93        96\n",
            "weighted avg       0.93      0.93      0.93        96\n",
            "\n",
            "[[52  6]\n",
            " [ 1 37]]\n",
            "\n",
            "***Linear SVM*****\n",
            "Score: 0.8645833333333334\n",
            "cross_val_scores: [0.93171296 0.98233323 0.91251885 0.96401515 0.61449275]\n",
            "Accuracy: 0.88 (+/- 0.27)\n",
            "0.8645833333333334\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.84      0.88        58\n",
            "           1       0.79      0.89      0.84        38\n",
            "\n",
            "   micro avg       0.86      0.86      0.86        96\n",
            "   macro avg       0.86      0.87      0.86        96\n",
            "weighted avg       0.87      0.86      0.87        96\n",
            "\n",
            "[[49  9]\n",
            " [ 4 34]]\n",
            "\n",
            "***RBF SVM*****\n",
            "Score: 0.625\n",
            "cross_val_scores: [0.3655914  0.36956522 0.36956522 0.36666667 0.36666667]\n",
            "Accuracy: 0.37 (+/- 0.00)\n",
            "0.625\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      1.00      0.76        58\n",
            "           1       1.00      0.05      0.10        38\n",
            "\n",
            "   micro avg       0.62      0.62      0.62        96\n",
            "   macro avg       0.81      0.53      0.43        96\n",
            "weighted avg       0.77      0.62      0.50        96\n",
            "\n",
            "[[58  0]\n",
            " [36  2]]\n",
            "\n",
            "***Gaussian Process*****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Score: 0.9479166666666666\n",
            "cross_val_scores: [0.86117647 0.98233323 0.91314765 0.84920635 0.73990874]\n",
            "Accuracy: 0.87 (+/- 0.16)\n",
            "0.9479166666666666\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.93      0.96        58\n",
            "           1       0.90      0.97      0.94        38\n",
            "\n",
            "   micro avg       0.95      0.95      0.95        96\n",
            "   macro avg       0.94      0.95      0.95        96\n",
            "weighted avg       0.95      0.95      0.95        96\n",
            "\n",
            "[[54  4]\n",
            " [ 1 37]]\n",
            "\n",
            "***Decision Tree*****\n",
            "Score: 0.90625\n",
            "cross_val_scores: [0.85748792 0.96446078 0.85939394 0.90949508 0.62975564]\n",
            "Accuracy: 0.84 (+/- 0.23)\n",
            "0.90625\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.90      0.92        58\n",
            "           1       0.85      0.92      0.89        38\n",
            "\n",
            "   micro avg       0.91      0.91      0.91        96\n",
            "   macro avg       0.90      0.91      0.90        96\n",
            "weighted avg       0.91      0.91      0.91        96\n",
            "\n",
            "[[52  6]\n",
            " [ 3 35]]\n",
            "\n",
            "***Random Forest*****\n",
            "Score: 0.9166666666666666\n",
            "cross_val_scores: [0.79442509 0.98233323 0.87898659 0.94631083 0.64375   ]\n",
            "Accuracy: 0.85 (+/- 0.24)\n",
            "0.9166666666666666\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.95      0.93        58\n",
            "           1       0.92      0.87      0.89        38\n",
            "\n",
            "   micro avg       0.92      0.92      0.92        96\n",
            "   macro avg       0.92      0.91      0.91        96\n",
            "weighted avg       0.92      0.92      0.92        96\n",
            "\n",
            "[[55  3]\n",
            " [ 5 33]]\n",
            "\n",
            "***Neural Net*****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Score: 0.9270833333333334\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "cross_val_scores: [0.89464286 0.98233323 0.91251885 0.96401515 0.82320099]\n",
            "Accuracy: 0.92 (+/- 0.11)\n",
            "0.9270833333333334\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.90      0.94        58\n",
            "           1       0.86      0.97      0.91        38\n",
            "\n",
            "   micro avg       0.93      0.93      0.93        96\n",
            "   macro avg       0.92      0.94      0.93        96\n",
            "weighted avg       0.93      0.93      0.93        96\n",
            "\n",
            "[[52  6]\n",
            " [ 1 37]]\n",
            "\n",
            "***AdaBoost*****\n",
            "Score: 0.90625\n",
            "cross_val_scores: [0.91274771 0.9469997  0.82424242 0.94631083 0.71994759]\n",
            "Accuracy: 0.87 (+/- 0.17)\n",
            "0.90625\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.90      0.92        58\n",
            "           1       0.85      0.92      0.89        38\n",
            "\n",
            "   micro avg       0.91      0.91      0.91        96\n",
            "   macro avg       0.90      0.91      0.90        96\n",
            "weighted avg       0.91      0.91      0.91        96\n",
            "\n",
            "[[52  6]\n",
            " [ 3 35]]\n",
            "\n",
            "***Naive Bayes*****\n",
            "Score: 0.9270833333333334\n",
            "cross_val_scores: [0.98254954 0.98233323 0.82738095 0.81777494 0.76315789]\n",
            "Accuracy: 0.87 (+/- 0.18)\n",
            "0.9270833333333334\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.95      0.94        58\n",
            "           1       0.92      0.89      0.91        38\n",
            "\n",
            "   micro avg       0.93      0.93      0.93        96\n",
            "   macro avg       0.93      0.92      0.92        96\n",
            "weighted avg       0.93      0.93      0.93        96\n",
            "\n",
            "[[55  3]\n",
            " [ 4 34]]\n",
            "\n",
            "***QDA*****\n",
            "Score: 0.8854166666666666\n",
            "cross_val_scores: [0.9297619  0.92795031 0.89338235 0.78564103 0.68013468]\n",
            "Accuracy: 0.84 (+/- 0.19)\n",
            "0.8854166666666666\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.95      0.91        58\n",
            "           1       0.91      0.79      0.85        38\n",
            "\n",
            "   micro avg       0.89      0.89      0.89        96\n",
            "   macro avg       0.89      0.87      0.88        96\n",
            "weighted avg       0.89      0.89      0.88        96\n",
            "\n",
            "[[55  3]\n",
            " [ 8 30]]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVtpPCBR9F8e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "correct = []\n",
        "incorrect = []\n",
        "\n",
        "def predict_from_file_mfccs(wav_file, model, show_results=False):  \n",
        "  X, sample_rate = librosa.load(wav_file, res_type='kaiser_fast')\n",
        "  mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
        "  X = [mfccs]\n",
        "  \n",
        "  file_cat = wav_file.split(\"/\")[-2]\n",
        "  prediction = model.predict(X)\n",
        "  \n",
        "  if show_results:\n",
        "    print(file_cat)\n",
        "    print(prediction)\n",
        "    print(\"\\n\")\n",
        "  \n",
        "  is_correct = False\n",
        "  if (file_cat == \"beeps\" and prediction == 0) or (file_cat == \"non-beeps\" and prediction == 1):\n",
        "    is_correct = True\n",
        "\n",
        "  return is_correct\n",
        "\n",
        "def run_predictions_mfccs(classifiers):\n",
        "  for classifier in classifiers:\n",
        "    num_correct = 0\n",
        "\n",
        "    print(\"predction using classifier\", classifier)\n",
        "    path = glob.glob(\"drive/My Drive/amd-training/sample-files/*/*\")\n",
        "    for file in path:\n",
        "      is_correct = predict_from_file_mfccs(file, classifier)\n",
        "      num_correct += is_correct\n",
        "\n",
        "    print(\"{} correct out of {}\".format(num_correct,len(path)) )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8q-OYiUUILid",
        "colab_type": "text"
      },
      "source": [
        "### Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3D8i8dEzspk",
        "colab_type": "code",
        "outputId": "8c1d07e4-fe23-4bf4-de43-7a0c3f5d3d38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "run_predictions_mfccs(classifiers)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predction using classifier KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
            "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
            "           weights='uniform')\n",
            "37 correct out of 38\n",
            "predction using classifier SVC(C=0.025, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
            "  shrinking=True, tol=0.001, verbose=False)\n",
            "31 correct out of 38\n",
            "predction using classifier SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=2, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "9 correct out of 38\n",
            "predction using classifier GaussianProcessClassifier(copy_X_train=True,\n",
            "             kernel=1**2 * RBF(length_scale=1), max_iter_predict=100,\n",
            "             multi_class='one_vs_rest', n_jobs=None,\n",
            "             n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",
            "             random_state=None, warm_start=False)\n",
            "35 correct out of 38\n",
            "predction using classifier DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "33 correct out of 38\n",
            "predction using classifier RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "            max_depth=5, max_features=1, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
            "            oob_score=False, random_state=None, verbose=0,\n",
            "            warm_start=False)\n",
            "24 correct out of 38\n",
            "predction using classifier MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
            "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
            "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
            "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
            "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
            "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
            "       validation_fraction=0.1, verbose=False, warm_start=False)\n",
            "35 correct out of 38\n",
            "predction using classifier AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
            "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
            "36 correct out of 38\n",
            "predction using classifier GaussianNB(priors=None, var_smoothing=1e-09)\n",
            "13 correct out of 38\n",
            "predction using classifier QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
            "               store_covariance=False, store_covariances=None, tol=0.0001)\n",
            "9 correct out of 38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69OA5Nw4K6lQ",
        "colab_type": "text"
      },
      "source": [
        "### Test Audio Samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faOMOgijAq-o",
        "colab_type": "code",
        "outputId": "f004be5c-7b2a-40a8-be33-66d14494ccf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import IPython.display as ipd\n",
        "#beep\n",
        "file = \"drive/My Drive/amd-training/sample-files/beeps/rec-c96ee63d346b4c678eb4f57721197398-20190129T160336-beep.wav\"\n",
        "ipd.Audio(file)\n",
        "print(predict_from_file(file, fit_model))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive/My Drive/amd-training/sample-files/beeps/rec-c96ee63d346b4c678eb4f57721197398-20190129T160336-beep.wav\n",
            "[0]\n",
            "\n",
            "\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AguF4ECjAv12",
        "colab_type": "code",
        "outputId": "16e63840-f732-4221-d3dc-86f2e979f5a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import IPython.display as ipd\n",
        "#beep\n",
        "file = \"drive/My Drive/amd-training/sample-files/non-beeps/rec-c96ee63d346b4c678eb4f57721197398-20190129T160017.wav\"\n",
        "ipd.Audio(file)\n",
        "print(predict_from_file(file, fit_model))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive/My Drive/amd-training/sample-files/non-beeps/rec-c96ee63d346b4c678eb4f57721197398-20190129T160017.wav\n",
            "[0]\n",
            "\n",
            "\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOkwqW6hW2Sz",
        "colab_type": "text"
      },
      "source": [
        "## Training With mutliple features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-jqmwFhYJBk",
        "colab_type": "code",
        "outputId": "e8b21345-b6be-4375-e442-73194951b86b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "features = ['mfccs_40','chroma', 'mel', 'contrast','tonnetz']\n",
        "X, y = generateFeaturesLabels(features, df)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total number of features 193\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBjTnXeFZRLU",
        "colab_type": "code",
        "outputId": "c400271d-f988-4bd3-8bf1-de2c41172bf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3335
        }
      },
      "source": [
        "multi_feature_classifiers = train_classifiers(X, y)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***Nearest Neighbors*****\n",
            "Score: 0.8129032258064516\n",
            "cross_val_scores: [0.93543956 0.85900542 0.76080833 0.87971781 0.8083404 ]\n",
            "Accuracy: 0.85 (+/- 0.12)\n",
            "0.8129032258064516\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.68      0.78        77\n",
            "           1       0.75      0.95      0.84        78\n",
            "\n",
            "   micro avg       0.81      0.81      0.81       155\n",
            "   macro avg       0.84      0.81      0.81       155\n",
            "weighted avg       0.84      0.81      0.81       155\n",
            "\n",
            "[[52 25]\n",
            " [ 4 74]]\n",
            "\n",
            "***Linear SVM*****\n",
            "Score: 0.9354838709677419\n",
            "cross_val_scores: [0.94651189 0.89284086 0.85857995 0.93487395 0.86015038]\n",
            "Accuracy: 0.90 (+/- 0.07)\n",
            "0.9354838709677419\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94        77\n",
            "           1       0.94      0.94      0.94        78\n",
            "\n",
            "   micro avg       0.94      0.94      0.94       155\n",
            "   macro avg       0.94      0.94      0.94       155\n",
            "weighted avg       0.94      0.94      0.94       155\n",
            "\n",
            "[[72  5]\n",
            " [ 5 73]]\n",
            "\n",
            "***RBF SVM*****\n",
            "Score: 0.5548387096774193\n",
            "cross_val_scores: [0.4949095  0.45507246 0.45294118 0.47320261 0.41032609]\n",
            "Accuracy: 0.46 (+/- 0.06)\n",
            "0.5548387096774193\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.10      0.19        77\n",
            "           1       0.53      1.00      0.69        78\n",
            "\n",
            "   micro avg       0.55      0.55      0.55       155\n",
            "   macro avg       0.77      0.55      0.44       155\n",
            "weighted avg       0.76      0.55      0.44       155\n",
            "\n",
            "[[ 8 69]\n",
            " [ 0 78]]\n",
            "\n",
            "***Gaussian Process*****\n",
            "Score: 0.9161290322580645\n",
            "cross_val_scores: [0.96799455 0.93590909 0.8454416  0.93487395 0.8816657 ]\n",
            "Accuracy: 0.91 (+/- 0.09)\n",
            "0.9161290322580645\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.88      0.91        77\n",
            "           1       0.89      0.95      0.92        78\n",
            "\n",
            "   micro avg       0.92      0.92      0.92       155\n",
            "   macro avg       0.92      0.92      0.92       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "[[68  9]\n",
            " [ 4 74]]\n",
            "\n",
            "***Decision Tree*****\n",
            "Score: 0.8580645161290322\n",
            "cross_val_scores: [0.9361413  0.89284086 0.84803922 0.91388889 0.86015038]\n",
            "Accuracy: 0.89 (+/- 0.07)\n",
            "0.8580645161290322\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.77      0.84        77\n",
            "           1       0.80      0.95      0.87        78\n",
            "\n",
            "   micro avg       0.86      0.86      0.86       155\n",
            "   macro avg       0.87      0.86      0.86       155\n",
            "weighted avg       0.87      0.86      0.86       155\n",
            "\n",
            "[[59 18]\n",
            " [ 4 74]]\n",
            "\n",
            "***Random Forest*****\n",
            "Score: 0.8709677419354839\n",
            "cross_val_scores: [0.9361413  0.86156112 0.85784832 0.85963079 0.83750728]\n",
            "Accuracy: 0.87 (+/- 0.07)\n",
            "0.8709677419354839\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.81      0.86        77\n",
            "           1       0.83      0.94      0.88        78\n",
            "\n",
            "   micro avg       0.87      0.87      0.87       155\n",
            "   macro avg       0.88      0.87      0.87       155\n",
            "weighted avg       0.88      0.87      0.87       155\n",
            "\n",
            "[[62 15]\n",
            " [ 5 73]]\n",
            "\n",
            "***Neural Net*****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Score: 0.8903225806451613\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "cross_val_scores: [0.89318182 0.90372141 0.79096179 0.90209381 0.82697674]\n",
            "Accuracy: 0.86 (+/- 0.09)\n",
            "0.8903225806451613\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.82      0.88        77\n",
            "           1       0.84      0.96      0.90        78\n",
            "\n",
            "   micro avg       0.89      0.89      0.89       155\n",
            "   macro avg       0.90      0.89      0.89       155\n",
            "weighted avg       0.90      0.89      0.89       155\n",
            "\n",
            "[[63 14]\n",
            " [ 3 75]]\n",
            "\n",
            "***AdaBoost*****\n",
            "Score: 0.8903225806451613\n",
            "cross_val_scores: [0.96805257 0.93605442 0.84732645 0.96768215 0.91396855]\n",
            "Accuracy: 0.93 (+/- 0.09)\n",
            "0.8903225806451613\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.82      0.88        77\n",
            "           1       0.84      0.96      0.90        78\n",
            "\n",
            "   micro avg       0.89      0.89      0.89       155\n",
            "   macro avg       0.90      0.89      0.89       155\n",
            "weighted avg       0.90      0.89      0.89       155\n",
            "\n",
            "[[63 14]\n",
            " [ 3 75]]\n",
            "\n",
            "***Naive Bayes*****\n",
            "Score: 0.8387096774193549\n",
            "cross_val_scores: [0.87022549 0.81144543 0.77339181 0.86839623 0.81410935]\n",
            "Accuracy: 0.83 (+/- 0.07)\n",
            "0.8387096774193549\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.70      0.81        77\n",
            "           1       0.77      0.97      0.86        78\n",
            "\n",
            "   micro avg       0.84      0.84      0.84       155\n",
            "   macro avg       0.87      0.84      0.84       155\n",
            "weighted avg       0.87      0.84      0.84       155\n",
            "\n",
            "[[54 23]\n",
            " [ 2 76]]\n",
            "\n",
            "***QDA*****\n",
            "Score: 0.7935483870967742\n",
            "cross_val_scores: [0.74656863 0.64812185 0.59913793 0.45294118 0.41032609]\n",
            "Accuracy: 0.57 (+/- 0.25)\n",
            "0.7935483870967742\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.97      0.82        77\n",
            "           1       0.96      0.62      0.75        78\n",
            "\n",
            "   micro avg       0.79      0.79      0.79       155\n",
            "   macro avg       0.84      0.79      0.79       155\n",
            "weighted avg       0.84      0.79      0.79       155\n",
            "\n",
            "[[75  2]\n",
            " [30 48]]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xzkFVInW5iJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_multiple_features_from_file(file, model,show_results=False):\n",
        "  X, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "  X = librosa.resample(X, sample_rate, 16000)\n",
        "\n",
        "  duration = librosa.get_duration(X, sample_rate)\n",
        "  stft = np.abs(librosa.stft(X))\n",
        "  mfccs_40 = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
        "  chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)    \n",
        "  mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate,n_mels=128,fmax=8000).T,axis=0)\n",
        "  contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "  tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),\n",
        "  sr=sample_rate).T,axis=0)\n",
        "\n",
        "  a = [mfccs_40, chroma, mel, contrast, tonnetz]\n",
        "  \n",
        "  \n",
        "  total_len = 0\n",
        "  for f in a:\n",
        "    total_len += f.shape[0]\n",
        " \n",
        "  features = np.vstack([np.empty((0,total_len)),np.hstack(a)])\n",
        "  \n",
        "  file_cat = file.split(\"/\")[-2]\n",
        "  prediction = model.predict(features)\n",
        "  \n",
        "  if show_results:\n",
        "    print(file)\n",
        "    print(prediction)\n",
        "    \n",
        "  is_correct = False\n",
        "  if (file_cat == \"beeps\" and prediction == 0) or (file_cat == \"non-beeps\" and prediction == 1):\n",
        "    is_correct = True\n",
        "\n",
        "  return is_correct\n",
        "  \n",
        "  \n",
        "def run_multi_features_predictions(classifiers):\n",
        "  for classifier in classifiers:\n",
        "    num_correct = 0\n",
        "\n",
        "    print(\"predction using classifier\", classifier)\n",
        "    path = glob.glob(\"drive/My Drive/amd-training/sample-files/*/*\")\n",
        "    for file in path:\n",
        "      is_correct = predict_multiple_features_from_file(file, classifier)\n",
        "      num_correct += is_correct\n",
        "\n",
        "    print(\"{} correct out of {}\".format(num_correct,len(path)) )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJpcEInUbHYb",
        "colab_type": "code",
        "outputId": "6dd91b92-850b-498f-e1f3-652ced6aadf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "run_multi_features_predictions(multi_feature_classifiers)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predction using classifier KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
            "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
            "           weights='uniform')\n",
            "36 correct out of 38\n",
            "predction using classifier SVC(C=0.025, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
            "  shrinking=True, tol=0.001, verbose=False)\n",
            "35 correct out of 38\n",
            "predction using classifier SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=2, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "10 correct out of 38\n",
            "predction using classifier GaussianProcessClassifier(copy_X_train=True,\n",
            "             kernel=1**2 * RBF(length_scale=1), max_iter_predict=100,\n",
            "             multi_class='one_vs_rest', n_jobs=None,\n",
            "             n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",
            "             random_state=None, warm_start=False)\n",
            "36 correct out of 38\n",
            "predction using classifier DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "36 correct out of 38\n",
            "predction using classifier RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "            max_depth=5, max_features=1, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
            "            oob_score=False, random_state=None, verbose=0,\n",
            "            warm_start=False)\n",
            "33 correct out of 38\n",
            "predction using classifier MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
            "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
            "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
            "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
            "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
            "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
            "       validation_fraction=0.1, verbose=False, warm_start=False)\n",
            "35 correct out of 38\n",
            "predction using classifier AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
            "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
            "35 correct out of 38\n",
            "predction using classifier GaussianNB(priors=None, var_smoothing=1e-09)\n",
            "37 correct out of 38\n",
            "predction using classifier QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
            "               store_covariance=False, store_covariances=None, tol=0.0001)\n",
            "17 correct out of 38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxelWq612yjX",
        "colab_type": "text"
      },
      "source": [
        "### Refactor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVByar1KTnFS",
        "colab_type": "text"
      },
      "source": [
        "Here, we're trying to see if combining the features into one array\n",
        "TLDR, no difference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzqOt3ID6W0G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def padarray(A, size):\n",
        "    t = size - len(A)\n",
        "    return np.pad(A, pad_width=(0, t), mode='constant')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHQxydj9H4GI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "l = 0\n",
        "features_list = ['mfccs_40', 'chroma', 'mel', 'contrast', 'tonnetz']\n",
        "for feature in features_list:\n",
        "  row = df[feature][0]\n",
        "  z = len(row)\n",
        "  if z > l:\n",
        "    l = z    \n",
        "l\n",
        "\n",
        "b = []\n",
        "c = []\n",
        "for index, row in df.iterrows():\n",
        "  a = []\n",
        "  for feature in features_list:\n",
        "    f = row[feature]\n",
        "    if len(f) < l:\n",
        "      a.append(padarray(f, l))\n",
        "    else:\n",
        "      a.append(row[feature])\n",
        "  c.append(row['label'])\n",
        "    \n",
        "  b.append(a)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8UX0Ujk4G_t",
        "colab_type": "code",
        "outputId": "c316d37c-71de-48bf-8718-702df7fef460",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "b = np.array(b)\n",
        "c = np.array(c)\n",
        "b.shape,c.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((279, 5, 128), (279,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1PBjDdsKb5F",
        "colab_type": "code",
        "outputId": "5826aae8-def2-4262-bb69-f4072cb2f821",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# X = np.array([[ x1, x2 ]])\n",
        "# print(X.shape)\n",
        "\n",
        "nsamples, nx, ny = b.shape\n",
        "B = b.reshape((nsamples,nx*ny))\n",
        "print(B.shape)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(B, c, test_size=0.33, random_state=42)\n",
        "\n",
        "# clf1 = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0)\n",
        "# clf1.fit(B, c)\n",
        "# X,y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(279, 640)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCPtQ6tsNiG9",
        "colab_type": "code",
        "outputId": "cfac0a35-864d-4f90-90f8-2a0260c3b10e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=42)\n",
        "fit_model = clf.fit(X_train, y_train)\n",
        "print(\"Score:\",fit_model.score(X_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Score: 0.9247311827956989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWSZAIrROxwt",
        "colab_type": "code",
        "outputId": "da3b2748-8b7c-4f76-9e0e-5f232e0a6da1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((186, 640), (93, 640), (186,), (93,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whch9dufPCUN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_from_file(file):\n",
        "  X, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "  duration = librosa.get_duration(X, sample_rate)\n",
        "  stft = np.abs(librosa.stft(X))\n",
        "  mfccs_40 = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
        "  chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)    \n",
        "  mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate,n_mels=128,fmax=8000).T,axis=0)\n",
        "  contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "  tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),\n",
        "  sr=sample_rate).T,axis=0)\n",
        "\n",
        "  l1 = 0\n",
        "  features = [mfccs_40, chroma, mel, contrast, tonnetz]\n",
        "  for feature in features:\n",
        "    z1 = len(feature)\n",
        "    if z1 > l1:\n",
        "      l1 = z1    \n",
        "  l1\n",
        "\n",
        "  b1 = []\n",
        "  c1 = []\n",
        "\n",
        "  a1 = []\n",
        "  for f in features:\n",
        "    if len(f) < l:\n",
        "      a1.append(padarray(f, l))\n",
        "    else:\n",
        "      a1.append(f)\n",
        "\n",
        "  b1.append(a1)\n",
        "  \n",
        "  b1 = np.array(b1)\n",
        "  b1.shape\n",
        "\n",
        "  nsamples, nx, ny = b1.shape\n",
        "  b1 = b1.reshape((nsamples,nx*ny))\n",
        "#   print(b1.shape)\n",
        "  \n",
        "  prediction = clf.predict(b1)\n",
        "  \n",
        "  file_cat = file.split(\"/\")[-2]\n",
        "  \n",
        "  print(file)\n",
        "  print(prediction)\n",
        "  if (file_cat == \"beeps\" and prediction == 0) or (file_cat == \"non-beeps\" and prediction == 1):\n",
        "    correct.append(file)\n",
        "  else:\n",
        "    incorrect.append(file)\n",
        "  print(\"\\n\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l-ua2apPekl",
        "colab_type": "code",
        "outputId": "5e9c16b7-44dd-44ac-8860-5a1453b33029",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2655
        }
      },
      "source": [
        "import glob\n",
        "index = 0\n",
        "correct = []\n",
        "incorrect = []\n",
        "# loaded_model = pickle.load(open(\"/content/RandomForestClassifier-20190502T1202.pkl\", \"rb\"))\n",
        "\n",
        "for file in glob.glob(\"drive/My Drive/amd-training/sample-files/*/*\"):\n",
        "  predict_from_file(file)\n",
        "  index = index+1\n",
        "  \n",
        "print(\"correct\",len(correct))\n",
        "print(\"incorrect\",len(incorrect))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "drive/My Drive/amd-training/sample-files/beeps/rec-838c2f2ae3ba4e72b5ec1846d42e216f-20190129T125904-beep.wav\n",
            "[0]\n",
            "\n",
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            "drive/My Drive/amd-training/sample-files/beeps/rec-838c2f2ae3ba4e72b5ec1846d42e216f-20190129T134024-beep.wav\n",
            "[0]\n",
            "\n",
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            "\n",
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            "drive/My Drive/amd-training/sample-files/beeps/rec-5017b54e7b76476098786a4112d9463b-20190129T134025-speech-beep.wav\n",
            "[1]\n",
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            "\n",
            "\n",
            "drive/My Drive/amd-training/sample-files/non-beeps/rec-d44b0840172545e38a8d5ef82cf67c67-20190129T173141.wav\n",
            "[1]\n",
            "\n",
            "\n",
            "correct 27\n",
            "incorrect 11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMGS6ZcLSewC",
        "colab_type": "code",
        "outputId": "b91fa71e-bbb8-46ed-fb83-1bc1d1abfdf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 640)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCsGYV9hQa3F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "save_model(trained_classifiers[-2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hv2p9msmRAPu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhNz-Z7ObFAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaVwsMkn5EpH",
        "colab_type": "text"
      },
      "source": [
        "## Audio Augmentation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLD9lL6f2gEg",
        "colab_type": "text"
      },
      "source": [
        "This section is to expirment on using audio augmentation on dataset\n",
        "augmentations are found here: https://www.kaggle.com/huseinzol05/sound-augmentation-librosa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rL-6bv9q7Gd3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def change_speed(wav_file, low=0.1, high=1):\n",
        "  audio = wav_file.copy()\n",
        "  # you can change low and high here\n",
        "  length_change = np.random.uniform(low, high)\n",
        "  speed_fac = 1.0  / length_change\n",
        "  print(\"resample length_change = \",length_change)\n",
        "  tmp = np.interp(np.arange(0,len(audio),speed_fac),np.arange(0,len(audio)),audio)\n",
        "  minlen = min(audio.shape[0], tmp.shape[0])\n",
        "  audio *= 0\n",
        "  audio[0:minlen] = tmp[0:minlen]\n",
        "  return audio\n",
        "\n",
        "def change_pitch(wav_file, sample_rate):\n",
        "  y_pitch = wav_file.copy()\n",
        "  bins_per_octave = 12\n",
        "  pitch_pm = 2\n",
        "  pitch_change =  pitch_pm * 2*(np.random.uniform())   \n",
        "  print(\"pitch_change = \",pitch_change)\n",
        "  y_pitch = librosa.effects.pitch_shift(y_pitch.astype('float64'), \n",
        "                                      sample_rate, n_steps=pitch_change, \n",
        "                                      bins_per_octave=bins_per_octave)\n",
        "  return y_pitch\n",
        "\n",
        "def add_noise(wav_file, amt=0.005):\n",
        "  y_noise = wav_file.copy()\n",
        "  # you can take any distribution from https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html\n",
        "  noise_amp = amt*np.random.uniform()*np.amax(y_noise)\n",
        "  y_noise = y_noise.astype('float64') + noise_amp * np.random.normal(size=y_noise.shape[0])\n",
        "  return y_noise\n",
        "\n",
        "def stretch(wav_file, amt=1.1):\n",
        "  input_length = len(wav_file)\n",
        "  streching = wav_file.copy()\n",
        "  streching = librosa.effects.time_stretch(streching.astype('float'), amt)\n",
        "  if len(streching) > input_length:\n",
        "    streching = streching[:input_length]\n",
        "  else:\n",
        "    streching = np.pad(streching, (0, max(0, input_length - len(streching))), \"constant\")\n",
        "  return streching"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCf2x1W9oJrW",
        "colab_type": "text"
      },
      "source": [
        "### Audio Sample tests"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZWknW-870Dt",
        "colab_type": "code",
        "outputId": "a2d3bb9b-3d11-4ce5-9289-fb64653e8a5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "from IPython.display import Audio\n",
        "\n",
        "audio, sample_rate = librosa.load(\"/content/drive/My Drive/amd-training/sample-files/beeps/41d929b9-59d2-4634-bdea-7fd7f4f18b05.wav\")\n",
        "Audio(audio, rate=sample_rate)\n"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppfA1fq2-5gO",
        "colab_type": "code",
        "outputId": "e265dbe3-2099-4d20-b83a-afcf4719c375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "Audio(change_speed(audio), rate=sample_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "resample length_change =  0.3702613474139048\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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Y6h0JFUnEWjIP5TSjY61p/f5Ph1v9mwaP3X5VbcukyqWp8cAEWNS6m+xXNImqt8WqXeWA6155/Ur8V/rZ95HGAvlxD2aBJl1vy8Cc0mWb5tk9TNC69Opa4w+nAPYzyNVYeZ2RlyUGqnJr5NhijquV/M2PthzrXeZTK0BYLVhhnuHXsC1C5pVpH2ca/IzUOFxnQGA4Zb72jwqjmuofqVZ+RQNxjqOi4f/rp745UORdVd4mURI9/OwhIihkVrJnxbBXXl5qWFrJGTYhp+di4mpre4WNaRlVWvRQef9YrvblCGd4c4sEsNNYWdCVichJdxIoyqSoK+W8Kw2qWUlbJNBTfxZuUWT+phizKUW+FX0coJftCT02U2gb41XpgTohjL3YvBy3E6ACOnDoJR7kk+97AHTQwtttWlkNo/x2LLhkpGif9Y/HF9V+mfHUA0aJtWpozye1bpM9CgxeFtrYY47iP8fw5ye66AMy+4Lu0YzZGtYWSmn5x+xN5yKrGrg6SDjUhNjOktuFbrT66Zlnly6DfN/L09a+GHZP9EDNMacojij7sqdC/g/uV5CVywnAul3swGd664t468mqlYuYXZGVAywzUKkcZ8nxIL+dz4uZPpbWjHy7ky0mpgapuPazhy2VUFpWk92FsHT3aKLlTO3KfnCNuhhQWYZPX37abkqmN+hF87PDtNM62rPW88oveMKrZyWaKtB5mcqAl0xZ6tGeQqOxySc6JrNw5YFWUINYz9cCy6f7Wi4TJ4HqsncER7wUB9jaEsqFevVLKa6mBi4nfbTNF1fKWWQPVv83722lxGdG8xgEjVReWq7Wrkk698wpV+Phqtj5xUuhWKVbvlM/QpVxQmbn5feuxwCxUUfa+lhlzDD7S6yV5bxpNTZ1x2kVvlpKFDoGGHTmKDOlOWzs/VBPPJnp2evPPv5BrqPlIyfPNGzFGxSVWvFWUEixNx1pyyUK6wy6Bgt9F7XathJcwsRx4KZ25aMveUBuEU/avhigDKe7DOv7JIno+/ZAyAXWdtrq1GBF1TRiJ+QlVq1nvZxOjRkW2hLQXn80bqRlfWavsjqEMRSTW/WXWkn8eAxpRqQMaqU5GMr3V9+bOZOaQ+uyT2dGJaZud38Gz94aJ9ozToa9OqxfJHim0XReRkOVi9xj1tQIHPXV5+OjgSt/e4SNvNnnW3VRMAB9b/elzCY4sU5DOVIrWleYOor8OWLqYGS26n84YAoKV2VbGhOtA62yV2bzZb4uhr8wT+zaidm+TdR8c+uEJH3nk/UrBpDWHRyWVmgHRTX/qEUknut+O2SNAlk1GuGRHoBir1BlSacvMkaC/5JtmqeYHMsKOZerCmUTqci37YlBVocbD5S1xDdy32d6JZLuxH9HD+CZLljUzke9zW5DZb2oOjPnhEETt5nplhaJfDgG623mXqtUegrKtxZUmiZR9MJ2cYinbucOMNkBAJBeGRVXEQuuu3ssrKY76Up28gfOlfkai1R3hYV0qCguJODsxL0QjZAYyRqy0Dl/Yq7K5XinPbLrg+QSmxp2VttJ7Xjlaq2mAeveeduKgNbSWi2R6IIsMcZnNSZcMMXBoJGmGneX04v1esHrjKRIaWc3GIhPVkma4NQxxW/0Hqeu5UGuln6ujnQZIhlMjgD+A+4sJQrnqLPahUMUeltYl3qJBTbaqT5k92r7ej7LTdifW0FSY0JbMQSlrSTSr0MA6ZEG2rpZl02He+dr4eSgqDz1egdxFeDbVlUVBlc0qie85S7tS31kDWFY9VndT7e/rK8cJZdnifLeg8fTNVq7FwtJn7jKKy5ltCsheUAJ7NYD2d6R6IMGMsonxOaFr67/sU/rWPLXjk2vPPmt5KamqO+1BEYn1KuaiJUdBzp1qqg2ZVRtZry5DWtY75naUCN/h692JrAnS7J3Q6OS7NpJFy8J/LiHKoqlOKpNOQ6KrZeW2suSj0M78b8l9iVWbx8AONBZWhdaK44bfG0sXSPHp6p04MZa1acbihYoB8/2rSgWY+urYDsHDWYZxtvn0dDAq287JEFlKrEqAv7S/xsoWDLLZvnrKqxkXOmF+HdJoldgm3QTmsR9Mg2meGVALsmAclEC2vYZGI1NPAUsJKUDKIr1bgbNVaobnhWrRoE06WdpJTYtDHzwzgxZKhn+EDH/eC8bpc5m7DI+gwzSdhmBlucKDLki6u4l8KsYOPyJ7xXSGbzSw4Qv88So/uc9rxg+f84jl++XwI3evfqun2awqMI1a0WBE59ZPxRvx0w3CWsfpxNtT7vly3VWA9jYEPvBdzHiZ5nninFYgPsQadkblyzLfXr8rKNm6qr6t+cIB9U+2ZDTPITvND+obOYAbix+HM5mmT0ZO06TfpruayWUZwMzYQTlVCRbNRX0CHf3sKkWpJDr5zqXCzuW9JnR0baBx3HRp1JnmvFGQXSQqFlB2BYMKrsxrGUlkqmINz+IENXtWchTi8VxNFYoU+ZRLvH+cI5WGKoYuU49Pi7uzibNqOz0SMT/0nrZt5UTSAr4P+o6JjHsWrptixYXYNn/kadCR3HjpySl8W92gGNQIhnD2SlNRLyTbIWls6he9VaG0BToGvuVVccbdRNnnWQb69D82Y572a9azlCXv0+usWUspstyswQ9k8Aawtcbido4HmldZGeqWzkRy0CYR9ut02dCrvGfpeUlZzAEgROSOVrXGIRMYfr4qwAkCGjh9k/IGdZ0G2qVX0YCNPOoWaVzLRM8kQ2MWOiZpZAsv6pvDGWrJuhyw8PP0uoapFdGykp5G+nn5EsqnjjripwXddoOky0DE3IvJvMlQ67t/8rRFBonWSiN/jvfbGKlKGhT9bEGz9VfmsZVd0c3NR6od2TlrG78KEzHGN8Z19DDAJvwLqaSZoCxh0LaUwCbK5fNi7w5VinWJF6p2XfvSUMXAFuUlAeEurM9ZyQlmO5S/3QP3Nn62VROE/0Q7JgkeOeSdXSGdpT/28vWQ0e2dX0nteR1KyF7vg2wWZHax9EGAQnvSiTbZYTw4UMRU3+bzllAy5W5ZOlHox3pP/eSChLX0ptAlHbEGLJVJpPlBm8Xf8vQntqqmWINgXx8K6bjrGdSdTNHj5a/G8VWHkaO9W9oTCUdLN48Ks2f2dZbO9Ey/8NvRGX8JhBxscMD03fbedgVyku4j+n9pC3phXjlSs8XN9rGU0xCxjGjJcilmi+dQJXRTFrCWRtM7zuPLFVkySjbtl0HWlZFHGyVmkY9tAvnPiNV7Fv9BU7GWmoauhAMv7XuVGRT5jKybUQjU50bbtg6Ckd4r2nf5Jrp2DiNyrCYApwuE+PDgTHx5kMlc+6IQGbROhpXmSdNr/ywLL0kQOflNR1GvRUVGx3Vgoe+9cQpRWYTrKA744y818sZb5BvwWVwJiVnJoFxE0JZEucbP5iDS285dyqHI8rov3flClGYhR0EFLaEIfFMZMFkLi2TP5KRYtwWW4fPAPw8as5jO+ZctPHHqVdynURXNod8tNvnYmNqauF7SM3Hmpgb6tHGQPsu1+RFZZGxd0KJE66cbtjWjDM5w+n6Y3/oerd4yZRX0dwsk/xERnMRZiXkY238/k1PjdnL2ryPn732bZEkIGa2s4OFR5TCHCRYJYm6dxAoSWO6KrB5zExZ2Sob1tN0giKwtaSwZF1wAsGtkqOb2NmOjOm6YKrdI61nkXZriPjX6hvHFOWF5XOXpkfjrGxefUXPk1s5G2/Qvr6ELqVkteYB8uVEKdTqnAMXqkn0d9KosOMa6b/4vosqGFZcU5Qugz/wsiR+pGUuSUAeEfPbhFr1Tnw8JOuo40HnPnSmRtNW2xySFoxIDvVTJ1FjAmrqu7JOPVqqG+TRi8BI7u6kVuXbsVDCy9Pkm0ZXxUtoOWKp5uNOKFq3mUofWDUcRhVuREVyIiZ65BAtqL8fkJObO1pijwF9ESwy42mmG3QYRthV0VyHF18IJnUW527jaipIO1wNqxq3G8JR48D2Lh3kReVcsS7ENRTDHQJYaYo2t8RpPqMj6O74hErMmIFcLtODA2xxk6XTJJSvfcA2kSua6xjRjXu7teuzJImpADakB6BWb9s2FFZF7HRvaCWlti30fXTN1ljOGRROy35x73Ylwieqc5TDj5NFGoYWxMq6uOmqTOSSakn5SkoAV09btpMtQzRyUabZpdxvZj9gUE1Z6lkFTmV80KykpHkn17SvhjgVB9t8FloIGnZAaESki6uNO2LNlVjmGr5RGD+kb1Zl2OYjsYrD2hPPmyVXzUsPONEqCuP8KMC4eYrjWUPcWtRuBB5yGaZjI9atu/8S0K6cF9rlDoL9Smw9JBdmzvPchmvVkVyQ103I5HXrJ5sjeKmy+edMptnjHB4S74Hor0WknuUOcADCChNYnAoZOYvJubAp2OQ56F73OUkil6MdMVUeRPSysaW7JGwtej4gECpbIBs6zv/9L+ym49ynGvPhBcCVFxtrVuWIAvZm6JFjk2pjurMMn1kFm7PSsoGwL+Qk4eSQsBqCEJOoXToZqUx3+b3pjuNCJ5s2+knFl6ccPFTxREQy+KaVZC+tbX8NUFqbXVqaDpT9mSyeI8tmhXOZhf3VaNx7F7nIxHZf6AjkVurUOnqMSFlP3FES3QGEb59kXqUy77aBoVLMnJtaXIzWumOpy2M+Z632HUlQ19HckxYUBcU0EWcHY4+sm73sT1IalltzkJ2+qS0646xk9nGGRYkWFtz3GLoJw7bsZ/Iii+n9+YKMkNrmXIRTREKSbt/jFePb72KB41MTXO9aWgx9ua5puGJxp2p2zooQGNAdcZWpBTpyv2V64zds1X6MEMTbZJqzDya81ixE40SmDbSJRznWXJ2vl9KIlzWAJuEi5yqMO3UOXNsJnAuRu7/xbjckFqVmMNGDnZRbHCiYicrfOLNqNGSFqUS4d4pSVx4bXBPDw7Ly5abBpNqvH3+CkDCa+Rn8Dci8rSyGpTCnjvUeBt7VLpt31iiG1TTfqDrkIOuyO/ANo5oXmv9Q30BdLzvlCyYBsYuDA1MDm+NZC0s6OIRqK2RfKeA4U8pn1t/alNOnw41yu+cSJcLvBT/ZD9QZXtj5zWf83qzVZTXo9vUeBr7VbdsH1gmHarVo6HMlnez5O5DNO5iR2kGRUQEksAylyuZs8TlCUtJfWwkYpEtAeeWq3WSIaKK2kgkKFvCcThYmxaczVeZfY/ZsjL3sTyCa0RwVEFy+Cm09o/AlovHhRKPVFRz62P9KlvfuqAUimqkZ+TyMHRnvXR5UBYLRMBMj3COFLfMAGJL3nMGcNk7+O3NpvGFapcG1JwkIWXfffhd/BZyyhmQG4f9r1f43UJIdFx0cUGq9O+sfYlrkyDK0xbnV0x2A2PKJs7ZJ5tLhpGmAum+NNNrDXWgTiYIsr8TlFmUVL5vBhJMoW+EZqgxIuktre2P1J5b2Gsh81mkbTZZTR1Q1IyeDY2VrYfwtDnKaXRvBUj1/zy5ppC+l7/Hgg0cUgpy/WJeLPLgoKTljPehot8rK1Rk83BnUsES/MQmk0WO7bRW/CZEenC2al87qfOKsMWP45nK0AoaRVgbc55c+yEW1z+b5IhpqKftdDkEbcNxR0hRBBG6WY8lko/AXAxAUBN3umlRLj/jqqAHh8Of898PL0trlnlsVr8OkcKkj5iGO7ST/09Ls3qRcyo/3vESqIKBZJF+zhwfuGQHfb5hCCH6zfiSWoSPowTunTy2caB5eU5EA0G1vIWniUXBaBBMVzd7y2qtL23hi53hhBqf/N6TLGFoiXmrVMwPIsMckDWMn7a5AMdHQHEVbWc75fABqrKGkpgC1OkhF2Ewd9NbkxgIzteW04x/sBf1rj/bbnBwtkMV+12z5ovfkl7HahPgVcx0a2PDKoTg+Z8Chy+ikOEtMOtpNHU2UvoN+sRNlDmQ07kFAPpFnG57a+k5We8xrT2Kj5kq06QelVxlcfpbUh/f07Wc+ou1rOXwYzruaRpw80cv/7u7EJVJlq3E9wtfTstxvWRHL2Pl46MFiy6hod2uKPteSXHMVekRNMtbl7uNDbYi/AxDGWxOaCE9GfUZsTiPbpl9z5sYN1glcetd2iOQ1yegjowbqHLppjPraOtyF07/Bn28m5DOjum76whCUMN07Ws2NvDowKesiWucKthsJiJkQnc4W/oXActMk7CIGrGm+MhENnaecmg/g/XFrQKJnpMAyDsX5FmOdlRjvSb12BSZXoe5pRznLjS9bUZ4uVDnCNm76Ikijhu+gAhsUeJ1Y2pkNDvpBaXJhxebOdePJTpj23ZmW/AZY8xQlceG3K3q9LhAwnK7c3tIuvoXr5iIdpCjyTAViVgjfHFmtyhR26ydqYhXosvk3zOkatN2xlLmCpC+O4u1iwa6EAVqTvJ2QG6RNtDoMqMzhpCXC9SZJsFkNHlrXTgbMM0/lEmLLK4j9Ls/AW8cdHNGMfp8sVGMnpX5x7UVFFk4d8NkQig62tGb/IfipGnmejNJa1V1005hB5W8+o1UjdW91QdBTyF1uWrvNGXlb6ULiracddthKcxnoncSV00VuMh2kzmNk7Ta+TBDAHAVbSU/Hvdtr6eLEpYYyoIW+lcFd8BjaiWo2YudcondpdDokjRaaK90IU/ZB02+bI33jnW7uQLGTIt0Y20tOw7uoao1iZ2XbdQOIt1fjXapW5ca18+ampCOnrKI954+4my4a808Cvc9sxKQiJzhz+cWbVV6bkZcUCIO2hahi43irIrrPTMRZsJqpUeSCE3DGZdmlvq/7AR8RtVq7mHuMArr+qs9lYem3NpJIp9Y/W3eVt4YMtGZn8mUT7Pt8+46B2c1aV1BYv6nu6iSkphgy80OZUxQbfFfPCoy43GqmJNTp+/ivitTXwFtVk+8D6bJ9JgKk+C6QP08QQlsLWiJOVPzb7LckFuZVtEqHBdbMnOfWvMf0tM6nMiM1Kmx7dA3ZGzYcRhIjP9yua6RJ5RCxowQulK/cyRjiipC4Qmk7I6up+PgsignYe1t1U1eEaLKlJcWkOS26fywQkBtQmymPBP2n7L2j2ycwM8DGYJXoW9qXKEhztSwnyaQsKoN7gE14GHfagRJjQZ4v2uVb5fAwYcJQ0v/atlici/l5RGnY46Yo+3czCiBY/9wuVEUFC/LA5eekHW3BfwBRCJummm7PO7zxLDVjM2Xl8/LGHpXK3NVX40jotg1niuL3KjF6hQ2Z2h+bxFK2ARru/KPrpRAwLcHbU1XcFpnnzEU6ASqHY73oJrasiNiXypxVVQSFu/Mk5qgkY+zZffxPP9nwmgrPL/4J7k6jm2V38reFKpY8HIGYtYn/9rQoQyQvawb7NktB1wQZZBAHgWsyhuphKt4zToEDTTgNSAVHglbCmwGagfgA1n7mf0sC4oHN/mw+ev0HPG/9S/9lgZEC30IZABR/K73IPQO9k/5Sf/JAxkGawYzBAsCu/yK9yL2dPb++Ar9Zv9C/w4G4wmgBhEFeP/x+Gv3PPmZ+YX96QTeBuIGAgPS/WL+Vv/s/hj+YP7K/qz+Nf8i/x7/hv/l/8X/ZP+C/3b/6v46/2b/q/+z/6r/eP+g/+//v/99/y//Df///vf+F/+x/z//bv+I/1H/Pf8x/3b/If8s/3H/Xf9Y/1//YP+N/1//Zf9p/5T/Wf9r/5z/Jf8//1f/j/+Q/7T/yP+t/0L/T/87/zz/Z/95/5f/Uf9u/1n/d/+E/1L/WP9y/2n/bv9o/1b/Pv9k/5P/gP9Y/1P/dP9Z/1b/Z/9a/0X/Y/+Q/3r/Xf9n/4L/Yv9Z/1L/V/9f/3L/cP9d/1r/dv9d/3P/ov9f/03/ef9v/1H/Sv9c/2b/Zf+G/4T/ZP9a/1T/Zv9n/2b/e/9M/zH/Xv+M/43/c/9s/zn/Tv9e/1b/X/9q/3X/kf+D/1D/UP9p/2z/Q/9h/3r/c/9S/3f/ZP9V/23/dP9b/zz/Vv9p/5X/j/9b/0L/Nv9X/4v/jv9k/1H/Wf9y/2v/cf9s/2L/Qv83/27/jf9o/1D/c/9U/0b/eP+C/1r/VP9h/1b/a/91/2//R/9g/27/Q/9I/3z/n/9z/1X/Wf9s/2z/SP9I/2n/cv9q/2P/Vv9v/4z/ef92/0z/P/9m/1z/Mf9h/4f/nP9w/0r/a/9l/1z/YP+H/1v/VP90/27/XP9v/4L/bv9V/zP/Xv96/07/Yv9q/2j/Yf9w/3P/Zf9Z/2n/ev9Y/2n/Yf8+/1j/cf+V/4X/Vf9C/1v/ff9n/1j/Zf9d/z3/W/9j/4j/l/97/1r/Uf9A/0L/c/+D/4P/n/9o/0n/Hv9B/3//hf9u/1z/bP91/2j/Zf9i/2z/W/9V/3X/U/9Z/3z/eP91/1X/ZP9r/2f/Z/9W/17/Uf9o/3X/d/9c/0r/gv+S/2X/Uv9K/2D/gv9w/2L/S/9Q/0z/YP99/5//fv9c/3D/Uf9O/2T/ZP9Z/1H/nf+1/1X/Jf9l/1n/WP93/3r/ZP9n/1b/cf+K/13/O/9d/27/g/9y/2H/Uv9a/3b/T/9r/2H/Vf9z/3z/Yf9k/3H/XP9A/0b/XP+h/5//U/85/0f/ZP94/5b/dP87/1H/e/9n/2T/Zv9Y/2D/cP9r/33/hf9S/0D/Wf9k/3H/iP+M/17/Rf84/2D/e/9s/1z/cf93/4H/Xv9Q/17/ZP9W/07/av9z/2D/dP9z/1v/W/98/3L/XP94/1T/M/9N/4P/ff9j/03/dv9q/1f/dv9u/0r/T/9v/33/ZP9L/3D/cP9b/2r/if9k/3D/hf9q/z7/Pv9K/2z/af9X/2z/jP+g/4X/U/89/0H/X/9R/0X/h/+F/0z/Xf99/4b/V/8//2L/a/9t/2X/X/93/3r/Nv88/3X/gf+B/3r/dv9l/0H/Mf9O/4D/d/97/3z/cf9n/0j/Uv9+/0H/LP9q/53/o/9h/0//Vv9Z/0v/av9h/2v/iP9y/27/Vv9G/1T/ef92/3P/bP9b/yz/Wf+G/1P/YP95/37/aP9C/1v/mf9j/0n/gP9y/1b/Xv9p/2b/Wf9s/4H/bf9a/3f/VP9a/4P/YP9P/zb/X/+j/5T/S/89/27/gP98/1z/T/9f/2P/UP9Z/2X/cf94/3b/hv9K/0P/Xv+A/2f/XP9+/3H/PP8+/47/jv90/1X/S/9q/3P/fv9f/0L/YP9+/3H/Wv80/1r/jP+I/1j/SP92/5f/cP9L/0j/Yf9R/0z/b/+R/4D/RP9G/3P/cf+B/2f/Lf9P/33/bP9R/2v/k/+E/0T/Lv90/4b/e/9f/1L/Z/9h/2T/Zv9u/1X/YP9V/2T/bf+B/3X/eP9L/zb/V/9+/4r/b/9Y/0z/bP9V/0j/bP+R/4n/af9S/0D/Sv94/4n/YP9W/3r/Xf9y/4z/cf9A/1j/av9r/1j/Pv9j/4L/df9i/2z/d/9k/1H/X/9s/2r/Wf9d/0//af9w/2//j/95/yP/Rf9o/1f/bf+N/3f/K/9S/4n/Sf9G/2z/dP9x/3T/g/9h/1b/Xf9P/2P/X/93/3H/Zv9U/1j/UP9s/4j/dP9x/2T/bf9e/1L/TP9u/2v/Uv9K/2r/h/+D/3H/YP9A/1L/bP9j/2H/d/9n/yb/Y/+O/3j/gv9p/yr/SP+C/2n/Qv9L/2T/i/+M/2r/Q/92/2X/U/9i/2b/Yv9//3b/Uf8//1b/hv92/1X/av9u/3L/T/81/2b/gf+K/4b/Yf9i/0n/SP9p/2r/aP9f/3D/Zf9A/1z/gv93/2L/Y/9y/3L/XP9K/zP/Z/92/5z/j/9Y/zz/Qv9a/4X/ev9n/1//Uf9k/2P/ev9+/2n/aP8t/z3/bP+c/4r/Zf9Q/2T/aP9V/2v/d/9R/0//Zv9t/3P/i/9t/0H/V/9w/2j/XP9w/3z/W/9R/17/Xf9C/1j/av+R/4D/dP9+/2z/S/9H/yL/W/+B/4z/b/96/23/TP9T/07/U/9x/2r/ZP92/1//iv+d/3b/Nf8d/zX/bv+H/4z/YP9p/2n/Wv9P/2n/ev9v/2T/eP9l/0n/Z/92/0L/MP9x/5v/kf9G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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK0tKvRKAROz",
        "colab_type": "code",
        "outputId": "b016713b-5acd-4dd6-c3cb-118744fabdfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "Audio(change_pitch(audio), rate=sample_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pitch_change =  1.5955860907844683\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unVnxWYDAs8S",
        "colab_type": "code",
        "outputId": "14a0165c-0353-4de5-9d3c-eec438dfa2dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "Audio(stretch(audio, 0.5), rate=sample_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQS5ET1mBVqx",
        "colab_type": "code",
        "outputId": "b4b886a5-b7c3-4c08-c3bc-81dab9b478fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "Audio(add_noise(audio, 0.01), rate=sample_rate)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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/z7/cf+b/+3+hf9S/4P/cP9y/xf/Of87/1X/bP+C/2n/P/87/2T/WP/p/oH/yv8Q/4b/l/87/2D/r/9K/w7/5P90//j+UP+P/4j/bP+c/yH/Uf87/53/af9R/3r/Sv9L/07/lv+p/2b/TP8F/0H/N/+G/2L/dv9k/4X/C/82/7j/Yf+G/83/iv9e/3z/gP9f/7T/kf+d/2T/d/9O/0//Mv9X/6v/RP8n/23/N/91/4b/GP96/6n/gv9n/6D/PP9//zr/cf+s/1T/Q/+i/7j/PP9x/8j///55/5n/ev9P/53/X/8y/3L/S/+V/57/fP9C/yv/iv9J/1v/p/94/y//iv9D/1b/tv+y/xL/Av+m/zf/mv91/4v/4P4Y/3r/Zv9S/4H/lv87/3b/h/8g/+b+QP82/3b/ef8r/xD/iP9s/0b/h/+F/4P/sv9K/4H/cv+A//P+mP/A/zr/af9O/4//cf9p/3D/bf9H/1T/Yf8g/0v/pf9Q/6D/Xv9Y/4f/bv97/2D/SP+I/4X/X/9O/+n/Rf+T/5j/hf8B/57/s/8B/2//jP9//0X/w//A//f+jv/X/xP/PP94/4r/8/5L/7v/Zv9Y/17/Wf9g/1P/hP9P/2T/j/9a/5f/Gf8Y/1r/Tf8z/2L/yv9v/0b/af9Z/0r/Kv96/2//bP99/6D/aP9//zv/Qf+f/7v/Rv95/6T/7P79/rT/Rv8t/0v/n/8y/zj/Of/t/n//j/9M/1n/kf84/6j/Uv9n/0z/df9U/23/ev9J/zr/Zv9r/xX/dv9I/4v//P53/4X/D/9Y/23/Tv8H/6X/fP86/2H/f/8+/4L/b/90/33/ef9q/2P/T/9O/7H/Yv9J/3v/KP9B/6j/Hf95/5H/Nv9r/5D/QP91/4z/c/+2/4r/lv+e/3v/DP9w/5f/HP8s/1z/R/9M/4D/if9V/07/Wf+J/2D/YP/s/2X/Sf+B/1n/Dv+G/7v/3v5m/5T/iv89/9H/vf9x/2v/6P+M/y3/dP/L/33/Bf9X/8r/TP86/7n/SP8v/6D/Wf/r/nT/T/9t/3X/nv8B/47/L/9c/5b/Uv9i/37/NP9Q/2z/ev9x/z//Rv9w/xP/vv9t/3b/i/+u/2D/bf/q/3n/Sv91/2n/U/9I/1X/l/8w/33/0P9X/y3/3/8A/z7/gf9g/xL/rP+i/zj/Mv9Q/1//Yf9E/1n/lv9p/xH/eP9x/7L/Mv9W/2//Hf9w/2T/LP9l/4H/Rv9G/wwAVf8D/8z/X/84/6n/VP8i/0v/af9q/47/Tv9S/3//f/9r/5H/bv8//6P/iP86/7H/qv9G/9v/W/9K/03/Zf99/yj/Pf9W/7r/jv9//03/5f89/1X/Cf+c/1D/Vv/H/3z/NP9z/5T/VP9a/1X/M/9F/3D/kP8P/2v/ov95/xP/jf+c/yf/OP+N/2D/aP+C/3//OP9H/5H/Qv9a/3f/fv/j/hL/AgCl/4X/Zf9+/3L/gP9T//r+Tf9T/1P/T/8g/0v/WP+Y/1X/tP+S/1D/Vf9w/1T/Qf9m/4P/Iv9o/27/L/+K/4P/b/82/4f/Sv9g/w7/Q/9n/y7/Nf8M/4P/W/95/9//Z/8kAA==\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRh9w5gkoPrG",
        "colab_type": "text"
      },
      "source": [
        "### Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtA0NtoM2pvK",
        "colab_type": "text"
      },
      "source": [
        "we'll take the audio samples from google drive, write to the local dir of this notebook, and add augmentation for all samples. We'll have 2 sample datasets, one thats augmented, and one that is the orginal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUaUq5rEUP2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import shutil\n",
        "shutil.rmtree('output', ignore_errors=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzAly7rdKHvH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def process(file, label, augment=False):\n",
        "  try:\n",
        "    print(\"load file {}\".format(file))\n",
        "    # here kaiser_fast is a technique used for faster extraction\n",
        "    \n",
        "    X, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "    X = librosa.resample(X, sample_rate, 16000)\n",
        "    if augment:\n",
        "      X = change_speed(X)\n",
        "      X = change_pitch(X, sample_rate)\n",
        "      X = stretch(X, float(np.random.uniform(0.5, 1, 1)))\n",
        "      X = add_noise(X, float(np.random.uniform(0.1, 1, 1)))\n",
        "      \n",
        "    return X, 16000\n",
        "  \n",
        "  except Exception as e:\n",
        "    print(\"Error encountered while parsing file: \", e)\n",
        "    \n",
        "def download_files(path, label, augment=False, max=150):\n",
        "  index = 0\n",
        "  directory = \"output/\"+label\n",
        "  try:\n",
        "    if not os.path.exists(directory):\n",
        "      os.makedirs(directory)\n",
        "  except:\n",
        "    pass\n",
        "  \n",
        "  for file in glob.glob(path):\n",
        "    if index <=max:\n",
        "      audio, sample_rate = process(file, label, augment)\n",
        "      if augment:\n",
        "        file_name = \"_augmented_\"+file.split(\"/\")[-1]\n",
        "      else:\n",
        "        file_name = file.split(\"/\")[-1]\n",
        "\n",
        "      librosa.output.write_wav(directory+\"/\"+file_name, audio, sample_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbyVmwkm5Hfm",
        "colab_type": "code",
        "outputId": "0370519e-951c-4469-f33a-403e7000d1d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16561
        }
      },
      "source": [
        "import glob, os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/answering-machine/*.wav\", label=\"beep\")\n",
        "download_files(\"drive/My Drive/amd-training/careangel-recordings/*.wav\", label=\"beep\")\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/answering-machine/*.wav\", label=\"beep\", augment=True)\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/speech-recordings/*.wav\", label=\"speech\")\n",
        "download_files(\"drive/My Drive/amd-training/answeringmachinemessages/*.mp3\", label=\"speech\")\n",
        "\n",
        "download_files(\"drive/My Drive/amd-training/speech-recordings/*.wav\", label=\"speech\",augment=True)\n"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "load file drive/My Drive/amd-training/answering-machine/8cc2e697-18ac-490d-b111-c90350708684.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e5030474-e71d-42ef-ae19-9de293a60c05.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/55b654e5-7d9f-4132-bc98-93e576b2d665.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/77310681-9ccf-4da7-bf55-dda0815dcf04-2.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/cbbc6d2a-5bf3-4ca4-9920-a12ad6d75d9e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e54d1f7e-d1c9-4982-80e9-eeb56e714840.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/359198a9-a8f1-44b6-a4f4-8383c85fc49d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/abe96bb3-4304-402e-a88b-7de453c31ce1.wav\n",
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            "load file drive/My Drive/amd-training/answering-machine/f0c3a2ca-4a89-4b0f-a20f-dccf6f756a0c.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/24c0d8e4-496f-4f8b-b8a3-2a0b61f0ce72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e19685f0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/c2823a7a-87f9-4689-9b53-d37f76964243.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/97a64f29-34c6-4638-936b-fb788f1c9011.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/38c5b9ca-9644-4672-bd14-d5ad0983f528.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/6d0c8d00-0202-11e9-bb68-51880c8718e4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e7b49010-97b4-4a03-af7b-5ee4606a4311.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/d8d9c220-02fa-11e9-96e0-fb0b86aa2d7e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b11ab910-02dc-11e9-8fd9-1d874d96e575.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
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            "load file drive/My Drive/amd-training/answering-machine/75ea624b-8ab9-4e17-9000-70e96166642a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a54eb99a-3bb3-456f-bed8-90e929f0cc90.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f78dbcc0-02dd-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/3dbc0174-3084-4641-92ac-db7ae40b3c20.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/d04d669f-6107-405b-8b82-b5327cf34cec.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/72642a94-b9a6-4084-b696-af9e37bd0b01.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/174add6a-1bca-4724-9524-3afb55a4966d.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f69e1982-0acf-4281-95a1-b5151aa3c2ad.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/97d83725-4626-4d99-b691-a3d67d3f6ced.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/370e97a8-f253-4b47-a647-9a103c0618ef.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/280215f0-02f1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8c453cd4-eea0-47e7-8489-2fa34197868b-old1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/167f2373-d222-4089-893b-76d4d2a1116a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/df101063-0935-4a03-be60-ea495a48fc3b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/07a3d677-0fdd-4155-a804-37679c039a8e.wav\n",
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            "load file drive/My Drive/amd-training/answering-machine/3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/0fc18be5-72e7-48c9-a17c-7aa3af659c0c.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8aa7d5ff-b936-49c2-a1d5-bcfa73587bcd-old1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/a7e954c0-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/77310681-9ccf-4da7-bf55-dda0815dcf04.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/96267e1a-b077-4fa7-bb1a-e31bb73ef2ae.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2bbc5b10-7c47-4747-a5c6-b4aabb845d8b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/0c9d15d7-876f-4915-896e-2cbd8547f569.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/470d36a0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/441d0f84-2685-4c40-acc7-37f08b251df5.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/24ef7595-0cd8-4637-b59f-276114b07a4e.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2a685eda-8dd9-4a4d-b00e-4f43715f81a4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/e11b5295-fe30-47c0-a9a8-309c954891cc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/f7b071ce-c6d9-4973-a4ac-ffc1996d6141.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b-2.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b5ff3b00-02e5-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/699603ae-8f1d-4260-9b25-6132c5c6fe3a.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/7eaeb600-0202-11e9-bb68-51880c8718e4.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/1f8d4133-83e7-4afa-a7be-3233993761aa.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/8917c4b3-51fa-4456-9384-65678f6e19f9.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-e149b51a4e1d4bb7b67f2f05c072dcb5-20190109T145619.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/41d929b9-59d2-4634-bdea-7fd7f4f18b05.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-c27052dcc9544aeba68712d93fd3f238-20190424T152320.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/1e194459-a1f1-4320-b580-d7cd7f89e5fc.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/fbae314e-6ef2-4cd7-ae2a-725ddd43c890.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/6677c6bf-1e9f-4e8d-a1ca-7fec9a091702.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/b728940b-dd5b-4764-bf7a-039399759795.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/41d929b9-59d2-4634-bdea-7fd7f4f18b05 (1).wav\n",
            "load file drive/My Drive/amd-training/answering-machine/1b1d6385-7a20-4c3b-9759-b216cb358c51 (1).wav\n",
            "load file drive/My Drive/amd-training/answering-machine/4d668e28-28d9-480a-9075-f0b4069a7020.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ec0dfded-d54e-4c96-838a-8c2b64157469-1.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/ec0dfded-d54e-4c96-838a-8c2b64157469.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-8587b299448c48989063c49ee5cf9d59-20190503T080106.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-fa42988185744bb0a675ec0049d122d5-20190503T080614.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-7f3a67f622bd47ac902dfbf4a3a89377-20190503T125031.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131537.wav\n",
            "load file drive/My Drive/amd-training/answering-machine/rec-39427f912abe42d29d4fb65312c8697e-20190503T125506.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T185259.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T185559.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T193729.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-daf7e06e976440478360f4ecd03819b0-20190123T184915.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T185600.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T185300.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T193730.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184919.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184919.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-bd3faaf86cc74dfab89b73a0ec6531de-20190123T124551.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184632.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T185258.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184410.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T184634.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T123507.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T183610.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T193725.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T185602.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184635.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T185302.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T124552.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184407.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184917.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T193727.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T185302.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-07006f1917444b48a69b30fc128cd0e6-20190123T193728.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T124549.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T124849.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-2aeed902d9dc41d08b249b41252d18d8-20190123T123507.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-374ab07de763469082229087a55b26c0-20190123T185223.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-3d6f5b0cb9f743fcbf0e8b8598bf99dc-20190123T184329.wav\n",
            "load file drive/My Drive/amd-training/careangel-recordings/rec-76f5ef4057dc4642807d4289c3944fde-20190123T123218.wav\n",
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            "load file drive/My Drive/amd-training/careangel-recordings/rec-d094529f933a45a69dd85f3dcc4b7d22-20190123T184559.wav\n",
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            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f457d760-02e3-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629fasdas.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a210698c-5e60-48bd-9ba3-c00981b0427a.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/abe96bb3-4304-402e-a88b-7de453c31ce1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e7b49010-97b4-4a03-af7b-5ee4606a4311sd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e7b49010-97b4-4a03-af7b-5ee4606a4311.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/df101063-0935-4a03-be60-ea495a48fc3b.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f7b071ce-c6d9-4973-a4ac-ffc1996d6141.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759e.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759ds.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5f2-de62afc471bf.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/f7b071ce-c6d9-4973-a4ac-ffc1996d6141w.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e54d1f7e-d1c9-4982-80e9-eeb56e714840.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/49298a15-27b4-4df2-bee4-2faf0a9191f8.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/96267e1a-b077-4fa7-bb1a-e31bb73ef2ae.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/472e7940-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/72642a94-b9a6-4084-b696-af9e37bd0b01.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/81270a2a-088b-4e3c-9f47-fd927a90b0abs.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/2595f8f0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/s.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/280215f0-02f1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/538549a3-18e8-40a0-9462-8f20cb1cee11.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/75ea624b-8ab9-4e17-9000-70e96166642a.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/77310681-9ccf-4da7-bf55-dda0815dcf04d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/77310681-9ccf-4da7-bf55-dda0815dcf04.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a2d34274-72a9-47f5-af9a-2f9bb4f8145fe.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/a2d34274-72a9-47f5-af9a-2f9bb4f8145f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011SD.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/174add6a-1bca-4724-9524-3afb55a4966d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/252aa640-02e1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/110ac98e-34fa-42e7-bbc5-450c72851db5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/232b3f2d-8c3a-44b2-97ec-915a5f3b6cf1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011W.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/370e97a8-f253-4b47-a647-9a103c0618ef.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011D.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/470d36a0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/55b654e5-7d9f-4132-bc98-93e576b2d665.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/33ce1bf0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/470d36a0-02e7-11e9-aa3d-ad1a095d8d72C.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/52a31be4-c4b1-46bc-9aa5-cb0090183bb6.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/441d0f84-2685-4c40-acc7-37f08b251df5.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/38c5b9ca-9644-4672-bd14-d5ad0983f528.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/55b654e5-7d9f-4132-bc98-93e576b2d665d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8cc2e697-18ac-490d-b111-c90350708684.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07dad.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903ded.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9e.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bccd.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07j.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6XN9trtsQGo-old1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99efe-ccaa85077f4d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1eb92c03-b085-4872-9b70-cb725cf7119d.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/0c9d15d7-876f-4915-896e-2cbd8547f569.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4de.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120836.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830 (1).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120842.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825 (1).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-1.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc (2).wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-3.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080101.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080058.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080210.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080225.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080241.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131546.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125458.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131547.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125504.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125501.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125505.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125459.wav\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-bacbcd61fa404c699a3ddc1bf4f1202c-20190503T143916.wav\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AdamSandlerPotSmokers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Africain.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AlzheimerAnsweringMachine.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AnsweringService.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AquaTeenHungerForce-Meatwad.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ArnoldSchwarzeneggerPizzaDelivery.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-AustinPowers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BipLeFlic.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BobAndTom.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-BritneySpears.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CantFindPhone.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CheechAndChong.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Chinois.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CledusTJudd-PotSmokers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Coca-Cola.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Confused.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-CradleOfFilth-Danis.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DaDaDa.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DaneCook.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DialAnAsshole.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-DonnieBaker.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ElvisPresley.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Eminem.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FBIvsTaliban.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FamilyGuy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Farts.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Flintstones.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-FriteSurLaLigne.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Garfield-NakedJello.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-GeorgeCarlin.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Hell.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-IndianGuyRingTone.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Irish.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JacquesChirac.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JerkyBoys.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-JohnnyHalliday.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-KittyWillDie.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LearnJapanese.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LeaveAMotherFuckingMessage-TheJerkyBoys.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Les7Nains.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LorenaBobbit.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-LouisDeFunes.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MachinesHomer.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MeetTheFockers.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MissionImpossible.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-ImBusy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-Insult.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MontyPython-SillyVoices.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-MultipleChoice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PMSHotline.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Paysan.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PorkyPig.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Prozac.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PsychiatricOffice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-PulpFiction.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-RenAndStimpy.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ScoobyDoo.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SecretMission.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SeinfeldGeorge.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SexStory.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-ShirleyQLiquor.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SilenceOfTheLambs.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SmithWessonAndMe.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-SpiceGirls.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-StarTrek.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Suicide.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Taliban.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-Tarzan.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TelephoneWarning.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TheSimpsons-SpringfieldPolice.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-TheYouthAhead.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-UnVoleur.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-UnitedStatesMarineCorps.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-WandaSykes.mp3\n",
            "load file drive/My Drive/amd-training/answeringmachinemessages/www.AnsweringMachineMessages.info-WonACar.mp3\n",
            "load file drive/My Drive/amd-training/speech-recordings/aa0b4100-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "resample length_change =  0.4026031798540216\n",
            "pitch_change =  3.3027777302453294\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05sdfs.wav\n",
            "resample length_change =  0.6354240832366842\n",
            "pitch_change =  3.4809230823387\n",
            "load file drive/My Drive/amd-training/speech-recordings/e3fa7fe0-02e2-11e9-aa3d-ad1a095d8d72asdas.wav\n",
            "resample length_change =  0.7501603247910421\n",
            "pitch_change =  1.4045288641252132\n",
            "load file drive/My Drive/amd-training/speech-recordings/e5030474-e71d-42ef-ae19-9de293a60c05asdsa.wav\n",
            "resample length_change =  0.16966132638826525\n",
            "pitch_change =  0.6497186008569464\n",
            "load file drive/My Drive/amd-training/speech-recordings/e11b5295-fe30-47c0-a9a8-309c954891ccasdas.wav\n",
            "resample length_change =  0.14923681197986507\n",
            "pitch_change =  1.6028323131190065\n",
            "load file drive/My Drive/amd-training/speech-recordings/d8d9c220-02fa-11e9-96e0-fb0b86aa2d7easdas.wav\n",
            "resample length_change =  0.8113155417589245\n",
            "pitch_change =  2.344225496305429\n",
            "load file drive/My Drive/amd-training/speech-recordings/e54d1f7e-d1c9-4982-80e9-eeb56e714840asdas.wav\n",
            "resample length_change =  0.4094220540672141\n",
            "pitch_change =  1.153657485549211\n",
            "load file drive/My Drive/amd-training/speech-recordings/b5ff3b00-02e5-11e9-aa3d-ad1a095d8d72sdfsd.wav\n",
            "resample length_change =  0.9477466327067092\n",
            "pitch_change =  2.280129323263147\n",
            "load file drive/My Drive/amd-training/speech-recordings/b11ab910-02dc-11e9-8fd9-1d874d96e575dsfsdf.wav\n",
            "resample length_change =  0.8204581150076972\n",
            "pitch_change =  3.346179539709521\n",
            "load file drive/My Drive/amd-training/speech-recordings/e19685f0-02e7-11e9-aa3d-ad1a095d8d72sdfsdf.wav\n",
            "resample length_change =  0.569488591447548\n",
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            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5f2-de62afc471bfsdfsd.wav\n",
            "resample length_change =  0.46799972718247385\n",
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            "load file drive/My Drive/amd-training/speech-recordings/ea9e20bf-9a45-4c9a-b5dddf2-de62afc471bf.wav\n",
            "resample length_change =  0.89834542813375\n",
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            "load file drive/My Drive/amd-training/speech-recordings/f457d760-02e3-11e9-aa3d-ad1a095d8d72dfds.wav\n",
            "resample length_change =  0.5732340917880142\n",
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            "load file drive/My Drive/amd-training/speech-recordings/f47cb7ab-4572-4f96-84ad-cda3c3a333c2.wav\n",
            "resample length_change =  0.21285028605941464\n",
            "pitch_change =  1.7530524330304984\n",
            "load file drive/My Drive/amd-training/speech-recordings/acdsf.wav\n",
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            "load file drive/My Drive/amd-training/speech-recordings/f0c3a2ca-4a89-4b0f-a20f-dccf6f756a0c.wav\n",
            "resample length_change =  0.35663695855374156\n",
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            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28.wav\n",
            "resample length_change =  0.6104474764397372\n",
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            "load file drive/My Drive/amd-training/speech-recordings/f457d760-02e3-11e9-aa3d-ad1a095d8d72.wav\n",
            "resample length_change =  0.12625840366696792\n",
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            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629fasdas.wav\n",
            "resample length_change =  0.331456761162335\n",
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            "load file drive/My Drive/amd-training/speech-recordings/a700f6ec-c6ee-4081-93a6-75fe275b7e28s.wav\n",
            "resample length_change =  0.8576815146999314\n",
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            "load file drive/My Drive/amd-training/speech-recordings/a210698c-5e60-48bd-9ba3-c00981b0427a.wav\n",
            "resample length_change =  0.7188213297629914\n",
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            "load file drive/My Drive/amd-training/speech-recordings/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "resample length_change =  0.5835173714030848\n",
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            "load file drive/My Drive/amd-training/speech-recordings/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "resample length_change =  0.7168018633068973\n",
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            "resample length_change =  0.17190266508620455\n",
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            "resample length_change =  0.9584921089218884\n",
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            "resample length_change =  0.702056648370964\n",
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            "resample length_change =  0.6500484770413775\n",
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            "load file drive/My Drive/amd-training/speech-recordings/fc2cd4b3-fe82-4e4e-82c1-ca820129b759ds.wav\n",
            "resample length_change =  0.20558678129665053\n",
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            "load file drive/My Drive/amd-training/speech-recordings/ea19ab7e-ad09-4736-8e68-f1fc06ca629f.wav\n",
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            "load file drive/My Drive/amd-training/speech-recordings/s.wav\n",
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            "load file drive/My Drive/amd-training/speech-recordings/97a64f29-34c6-4638-936b-fb788f1c9011W.wav\n",
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            "resample length_change =  0.6607897082438556\n",
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            "load file drive/My Drive/amd-training/speech-recordings/470d36a0-02e7-11e9-aa3d-ad1a095d8d72C.wav\n",
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            "resample length_change =  0.9765342332612563\n",
            "pitch_change =  1.2932450078989102\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "resample length_change =  0.9059817745859522\n",
            "pitch_change =  1.120155200469854\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9.wav\n",
            "resample length_change =  0.7952008005850228\n",
            "pitch_change =  0.07349292209037239\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "resample length_change =  0.988413122587884\n",
            "pitch_change =  2.3639151888027\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "resample length_change =  0.8501891957529386\n",
            "pitch_change =  1.7110578864876818\n",
            "load file drive/My Drive/amd-training/speech-recordings/8cc2e697-18ac-490d-b111-c90350708684.wav\n",
            "resample length_change =  0.501043017919795\n",
            "pitch_change =  0.5794051843877055\n",
            "load file drive/My Drive/amd-training/speech-recordings/7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "resample length_change =  0.3046074344887981\n",
            "pitch_change =  3.627663528376633\n",
            "load file drive/My Drive/amd-training/speech-recordings/18a7241e-03b9-4ca2-bc7e-6957dfdc07dad.wav\n",
            "resample length_change =  0.4583558438873211\n",
            "pitch_change =  0.043067640287901554\n",
            "load file drive/My Drive/amd-training/speech-recordings/8c625802-daa5-4a93-8f9e-337fce1903ded.wav\n",
            "resample length_change =  0.40117580863984925\n",
            "pitch_change =  0.6678112807416396\n",
            "load file drive/My Drive/amd-training/speech-recordings/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9e.wav\n",
            "resample length_change =  0.2613933907379088\n",
            "pitch_change =  1.5361962352852916\n",
            "load file drive/My Drive/amd-training/speech-recordings/18d388df-764c-4ff1-ad42-72801c079bccd.wav\n",
            "resample length_change =  0.29445283461302285\n",
            "pitch_change =  1.6259343079427495\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07j.wav\n",
            "resample length_change =  0.8508434342089957\n",
            "pitch_change =  1.0257308960889207\n",
            "load file drive/My Drive/amd-training/speech-recordings/6XN9trtsQGo-old1.wav\n",
            "resample length_change =  0.9555054000602642\n",
            "pitch_change =  0.9044986306468985\n",
            "load file drive/My Drive/amd-training/speech-recordings/6b95c6ec-d2e5-4dcf-a8aa-127737385d07.wav\n",
            "resample length_change =  0.39020095593494153\n",
            "pitch_change =  0.891059149857961\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "resample length_change =  0.3633985393735887\n",
            "pitch_change =  2.7642991879179037\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99efe-ccaa85077f4d.wav\n",
            "resample length_change =  0.3396192122936496\n",
            "pitch_change =  0.5458463193751704\n",
            "load file drive/My Drive/amd-training/speech-recordings/1eb92c03-b085-4872-9b70-cb725cf7119d.wav\n",
            "resample length_change =  0.516658060444889\n",
            "pitch_change =  1.5721886077337763\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "resample length_change =  0.36378067266343317\n",
            "pitch_change =  1.8525735574651554\n",
            "load file drive/My Drive/amd-training/speech-recordings/0c9d15d7-876f-4915-896e-2cbd8547f569.wav\n",
            "resample length_change =  0.3292453607823197\n",
            "pitch_change =  2.0449366425495623\n",
            "load file drive/My Drive/amd-training/speech-recordings/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4de.wav\n",
            "resample length_change =  0.8215636125766829\n",
            "pitch_change =  0.04847940577661003\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120836.wav\n",
            "resample length_change =  0.7841772418131837\n",
            "pitch_change =  0.7332525531767904\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830 (1).wav\n",
            "resample length_change =  0.9557729166005776\n",
            "pitch_change =  1.011679426901987\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120842.wav\n",
            "resample length_change =  0.8775771342942656\n",
            "pitch_change =  3.4756150118832996\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825.wav\n",
            "resample length_change =  0.19920246519139884\n",
            "pitch_change =  2.112084855980847\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830.wav\n",
            "resample length_change =  0.3361279386533635\n",
            "pitch_change =  0.5392014524841118\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825 (1).wav\n",
            "resample length_change =  0.5622354344931751\n",
            "pitch_change =  0.4087957956115513\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-1.wav\n",
            "resample length_change =  0.725981624740213\n",
            "pitch_change =  3.9515238816659974\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc (2).wav\n",
            "resample length_change =  0.9746878475235913\n",
            "pitch_change =  1.5650499574046157\n",
            "load file drive/My Drive/amd-training/speech-recordings/1e194459-a1f1-4320-b580-d7cd7f89e5fc-3.wav\n",
            "resample length_change =  0.8318923950459493\n",
            "pitch_change =  2.937640644561495\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080101.wav\n",
            "resample length_change =  0.6199611714733009\n",
            "pitch_change =  2.724379065783719\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080058.wav\n",
            "resample length_change =  0.7894744285836629\n",
            "pitch_change =  1.435399491113285\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080210.wav\n",
            "resample length_change =  0.38648803533655895\n",
            "pitch_change =  2.2449415161777173\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080225.wav\n",
            "resample length_change =  0.8403815693597807\n",
            "pitch_change =  3.4773126404683445\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-8587b299448c48989063c49ee5cf9d59-20190503T080241.wav\n",
            "resample length_change =  0.6242532332932078\n",
            "pitch_change =  0.4897365825327453\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131546.wav\n",
            "resample length_change =  0.1356205869380966\n",
            "pitch_change =  2.293041101062433\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125458.wav\n",
            "resample length_change =  0.8874200151791294\n",
            "pitch_change =  2.3082596013145933\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131547.wav\n",
            "resample length_change =  0.8811516981494613\n",
            "pitch_change =  2.306639850543094\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125504.wav\n",
            "resample length_change =  0.1424795907364396\n",
            "pitch_change =  1.3376558675533956\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125501.wav\n",
            "resample length_change =  0.6244641815890284\n",
            "pitch_change =  1.3488593239832296\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125505.wav\n",
            "resample length_change =  0.9848120179513429\n",
            "pitch_change =  3.3291416023748766\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-39427f912abe42d29d4fb65312c8697e-20190503T125459.wav\n",
            "resample length_change =  0.9174952883642131\n",
            "pitch_change =  0.8877989915506381\n",
            "load file drive/My Drive/amd-training/speech-recordings/rec-bacbcd61fa404c699a3ddc1bf4f1202c-20190503T143916.wav\n",
            "resample length_change =  0.40750880691308033\n",
            "pitch_change =  0.24442355348303613\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPp8j7CGM7dc",
        "colab_type": "text"
      },
      "source": [
        "### Verify sample augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3K-jF0MV3KzF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#grab a random sample to verify\n",
        "aug_file = \"output/beep/_augmented_07a3d677-0fdd-4155-a804-37679c039a8e.wav\"\n",
        "norm_file = \"output/beep/07a3d677-0fdd-4155-a804-37679c039a8e.wav\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnGIXaXg3dgD",
        "colab_type": "code",
        "outputId": "381bb3a6-9454-4407-f498-80fd5717dffb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "audio, sample_rate = librosa.load(norm_file)\n",
        "Audio(audio, rate=sample_rate)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsE7B0B33zme",
        "colab_type": "code",
        "outputId": "c731d973-d6ce-4e0e-f991-b7ce802f116c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "audio, sample_rate = librosa.load(aug_file)\n",
        "Audio(audio, rate=sample_rate)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RT0y-LA0NBQ9",
        "colab_type": "text"
      },
      "source": [
        "### Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQ74QK4PLetd",
        "colab_type": "code",
        "outputId": "8777c65e-7d2d-49ac-9400-fb58ad5fa638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7956
        }
      },
      "source": [
        "#build dataframe from files in \"output\" directory\n",
        "def process(file, label):\n",
        "  try:\n",
        "    print(\"load file {}\".format(file))\n",
        "    # here kaiser_fast is a technique used for faster extraction\n",
        "    \n",
        "    X, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "      \n",
        "    duration = librosa.get_duration(X, sample_rate)\n",
        "    stft = np.abs(librosa.stft(X))\n",
        "    mfccs_40 = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
        "    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)    \n",
        "    mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate,n_mels=128,fmax=8000).T,axis=0)\n",
        "    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),\n",
        "    sr=sample_rate).T,axis=0)\n",
        "    is_augmented = \"_augmented_\" in file\n",
        "    row = pd.Series({'file_path':file,'mfccs_40':mfccs_40, 'chroma':chroma, 'mel':mel, 'contrast':contrast, 'tonnetz':tonnetz, 'duration':duration,'label':label, 'augmented': is_augmented},name=8)\n",
        "    return row\n",
        "  \n",
        "  except Exception as e:\n",
        "    print(\"Error encountered while parsing file: \", file)\n",
        "    \n",
        "    \n",
        "df = pd.DataFrame(columns=['file_path', 'mfccs_40', 'chroma', 'mel','contrast','tonnetz', 'duration','label', 'augmented'])\n",
        "df = df.fillna(0) # with 0s rather than NaNs\n",
        "\n",
        "for file in glob.glob(\"output/beep/*.wav\"):\n",
        "  row = process(file,\"beep\")\n",
        "  df.loc[len(df)] = row\n",
        "\n",
        "for file in glob.glob(\"output/speech/*.wav\"):\n",
        "  row = process(file,\"speech\")\n",
        "  df.loc[len(df)] = row\n",
        "\n",
        "\n",
        "      "
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "load file output/beep/rec-92ec261b62d843dca160f9a25bdb2db6-20190123T183536.wav\n",
            "load file output/beep/6d0c8d00-0202-11e9-bb68-51880c8718e4.wav\n",
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            "load file output/beep/_augmented_8aa7d5ff-b936-49c2-a1d5-bcfa73587bcd-old1.wav\n",
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            "load file output/speech/280215f0-02f1-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9.wav\n",
            "load file output/speech/rec-8587b299448c48989063c49ee5cf9d59-20190503T080210.wav\n",
            "load file output/speech/5c6f6f13-8fd1-4dd1-99ef-ccaa85077f4d.wav\n",
            "load file output/speech/_augmented_rec-39427f912abe42d29d4fb65312c8697e-20190503T125458.wav\n",
            "load file output/speech/7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9e.wav\n",
            "load file output/speech/_augmented_e7b49010-97b4-4a03-af7b-5ee4606a4311.wav\n",
            "load file output/speech/d8d9c220-02fa-11e9-96e0-fb0b86aa2d7easdas.wav\n",
            "load file output/speech/rec-39427f912abe42d29d4fb65312c8697e-20190503T125459.wav\n",
            "load file output/speech/_augmented_1e194459-a1f1-4320-b580-d7cd7f89e5fc (2).wav\n",
            "load file output/speech/c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file output/speech/s.wav\n",
            "load file output/speech/5c6f6f13-8fd1-4dd1-99efe-ccaa85077f4d.wav\n",
            "load file output/speech/df101063-0935-4a03-be60-ea495a48fc3b.wav\n",
            "load file output/speech/18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "load file output/speech/_augmented_1e194459-a1f1-4320-b580-d7cd7f89e5fc-3.wav\n",
            "load file output/speech/acdsf.wav\n",
            "load file output/speech/_augmented_e11b5295-fe30-47c0-a9a8-309c954891ccasdas.wav\n",
            "load file output/speech/bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/_augmented_472e7940-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/_augmented_s.wav\n",
            "load file output/speech/_augmented_441d0f84-2685-4c40-acc7-37f08b251df5.wav\n",
            "load file output/speech/abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file output/speech/335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
            "load file output/speech/174add6a-1bca-4724-9524-3afb55a4966d.wav\n",
            "load file output/speech/26b25bb7-6825-43e7-b8bd-03a3884ed694.wav\n",
            "load file output/speech/_augmented_a2d34274-72a9-47f5-af9a-2f9bb4f8145fe.wav\n",
            "load file output/speech/_augmented_1eb92c03-b085-4872-9b70-cb725cf7119d.wav\n",
            "load file output/speech/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120825 (1).wav\n",
            "load file output/speech/33ce1bf0-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/rec-8587b299448c48989063c49ee5cf9d59-20190503T080241.wav\n",
            "load file output/speech/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830 (1).wav\n",
            "load file output/speech/_augmented_rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120836.wav\n",
            "load file output/speech/a700f6ec-c6ee-4081-93a6-75fe275b7e28.wav\n",
            "load file output/speech/rec-0b63afe6e14a4547ac2e5d75fb64d4bc-20190503T131547.wav\n",
            "load file output/speech/_augmented_rec-bacbcd61fa404c699a3ddc1bf4f1202c-20190503T143916.wav\n",
            "load file output/speech/_augmented_rec-8587b299448c48989063c49ee5cf9d59-20190503T080058.wav\n",
            "load file output/speech/_augmented_a700f6ec-c6ee-4081-93a6-75fe275b7e28.wav\n",
            "load file output/speech/e54d1f7e-d1c9-4982-80e9-eeb56e714840.wav\n",
            "load file output/speech/_augmented_3840b850-02e6-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/_augmented_rec-8587b299448c48989063c49ee5cf9d59-20190503T080225.wav\n",
            "load file output/speech/e7b49010-97b4-4a03-af7b-5ee4606a4311sd.wav\n",
            "load file output/speech/ea9e20bf-9a45-4c9a-b5f2-de62afc471bfsdfsd.wav\n",
            "load file output/speech/470d36a0-02e7-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/97a64f29-34c6-4638-936b-fb788f1c9011D.wav\n",
            "load file output/speech/_augmented_232b3f2d-8c3a-44b2-97ec-915a5f3b6cf1.wav\n",
            "load file output/speech/a700f6ec-c6ee-4081-93a6-75fe275b7e28s.wav\n",
            "load file output/speech/_augmented_fc2cd4b3-fe82-4e4e-82c1-ca820129b759e.wav\n",
            "load file output/speech/55b654e5-7d9f-4132-bc98-93e576b2d665.wav\n",
            "load file output/speech/_augmented_acdsf.wav\n",
            "load file output/speech/8c625802-daa5-4a93-8f9e-337fce1903de.wav\n",
            "load file output/speech/ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file output/speech/ea9e20bf-9a45-4c9a-b5dddf2-de62afc471bf.wav\n",
            "load file output/speech/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120836.wav\n",
            "load file output/speech/a2d34274-72a9-47f5-af9a-2f9bb4f8145fe.wav\n",
            "load file output/speech/_augmented_6XN9trtsQGo-old1.wav\n",
            "load file output/speech/_augmented_34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file output/speech/_augmented_b5ff3b00-02e5-11e9-aa3d-ad1a095d8d72sdfsd.wav\n",
            "load file output/speech/_augmented_abb95162-9f09-4b35-a890-6f4aa0e2b66b.wav\n",
            "load file output/speech/1e194459-a1f1-4320-b580-d7cd7f89e5fc (2).wav\n",
            "load file output/speech/rec-39427f912abe42d29d4fb65312c8697e-20190503T125504.wav\n",
            "load file output/speech/_augmented_ea19ab7e-ad09-4736-8e68-f1fc06ca629fasdas.wav\n",
            "load file output/speech/49298a15-27b4-4df2-bee4-2faf0a9191f8.wav\n",
            "load file output/speech/_augmented_18a7241e-03b9-4ca2-bc7e-6957dfdc07da.wav\n",
            "load file output/speech/_augmented_94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/55b654e5-7d9f-4132-bc98-93e576b2d665d.wav\n",
            "load file output/speech/94aed880-02df-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/34e02fdf-4ee6-4d19-b17f-b65de31d7e14.wav\n",
            "load file output/speech/_augmented_e7b49010-97b4-4a03-af7b-5ee4606a4311sd.wav\n",
            "load file output/speech/_augmented_rec-39427f912abe42d29d4fb65312c8697e-20190503T125504.wav\n",
            "load file output/speech/_augmented_bccc4780-02f8-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/_augmented_a700f6ec-c6ee-4081-93a6-75fe275b7e28s.wav\n",
            "load file output/speech/52a31be4-c4b1-46bc-9aa5-cb0090183bb6.wav\n",
            "load file output/speech/_augmented_75ea624b-8ab9-4e17-9000-70e96166642a.wav\n",
            "load file output/speech/_augmented_rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120842.wav\n",
            "load file output/speech/_augmented_6b95c6ec-d2e5-4dcf-a8aa-127737385d07j.wav\n",
            "load file output/speech/a210698c-5e60-48bd-9ba3-c00981b0427a.wav\n",
            "load file output/speech/_augmented_335519ea-8ce8-43db-8800-bdef53ca73e5.wav\n",
            "load file output/speech/_augmented_7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9.wav\n",
            "load file output/speech/_augmented_rec-39427f912abe42d29d4fb65312c8697e-20190503T125505.wav\n",
            "load file output/speech/f47cb7ab-4572-4f96-84ad-cda3c3a333c2.wav\n",
            "load file output/speech/_augmented_bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file output/speech/6b95c6ec-d2e5-4dcf-a8aa-127737385d07.wav\n",
            "load file output/speech/bc963fab-a32a-4714-af9e-31dd2f240b4b.wav\n",
            "load file output/speech/rec-8587b299448c48989063c49ee5cf9d59-20190503T080101.wav\n",
            "load file output/speech/18d388df-764c-4ff1-ad42-72801c079bcc.wav\n",
            "load file output/speech/_augmented_ea10246b-40f2-42ff-ae04-36604b8fd3fd.wav\n",
            "load file output/speech/81270a2a-088b-4e3c-9f47-fd927a90b0ab.wav\n",
            "load file output/speech/rec-39427f912abe42d29d4fb65312c8697e-20190503T125458.wav\n",
            "load file output/speech/rec-8587b299448c48989063c49ee5cf9d59-20190503T080225.wav\n",
            "load file output/speech/5c082690-02f2-11e9-aa3d-ad1a095d8d72.wav\n",
            "load file output/speech/_augmented_86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file output/speech/_augmented_1e194459-a1f1-4320-b580-d7cd7f89e5fc-1.wav\n",
            "load file output/speech/_augmented_7dbd920f-abea-4b7e-9fa0-3c0be2d31bb9e.wav\n",
            "load file output/speech/b11ab910-02dc-11e9-8fd9-1d874d96e575dsfsdf.wav\n",
            "load file output/speech/_augmented_6b95c6ec-d2e5-4dcf-a8aa-127737385d07.wav\n",
            "load file output/speech/ea9e20bf-9a45-4c9a-b5f2-de62afc471bf.wav\n",
            "load file output/speech/_augmented_c3c623be-581b-479f-896d-22f1dc1744b0.wav\n",
            "load file output/speech/_augmented_rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830.wav\n",
            "load file output/speech/rec-d31338b049e349ceb97b8ef7a10ab039-20190130T120830.wav\n",
            "load file output/speech/a2d34274-72a9-47f5-af9a-2f9bb4f8145f.wav\n",
            "load file output/speech/22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file output/speech/_augmented_22071349-44d6-448d-8d76-834db8d97475.wav\n",
            "load file output/speech/_augmented_d8d9c220-02fa-11e9-96e0-fb0b86aa2d7easdas.wav\n",
            "load file output/speech/86e364ff-93a6-44af-8118-080024e6b45d.wav\n",
            "load file output/speech/_augmented_fc2cd4b3-fe82-4e4e-82c1-ca820129b759ds.wav\n",
            "load file output/speech/e11b5295-fe30-47c0-a9a8-309c954891ccasdas.wav\n",
            "load file output/speech/_augmented_7a4d1842-1db6-455e-8928-0bf9878bde9f.wav\n",
            "load file output/speech/bd1d7bbe-87a6-4a03-a8a3-35b83df8e47b.wav\n",
            "load file output/speech/_augmented_e5030474-e71d-42ef-ae19-9de293a60c05sdfs.wav\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBsk45HjYH6L",
        "colab_type": "code",
        "outputId": "13c64a57-ac48-4414-e1ad-ef2e1846e196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 467 entries, 0 to 466\n",
            "Data columns (total 9 columns):\n",
            "file_path    467 non-null object\n",
            "mfccs_40     467 non-null object\n",
            "chroma       467 non-null object\n",
            "mel          467 non-null object\n",
            "contrast     467 non-null object\n",
            "tonnetz      467 non-null object\n",
            "duration     467 non-null float64\n",
            "label        467 non-null object\n",
            "augmented    467 non-null object\n",
            "dtypes: float64(1), object(8)\n",
            "memory usage: 36.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rItLrcu5SLs",
        "colab_type": "code",
        "outputId": "1cdee62f-6522-4a93-8187-ee7a5627ca42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#convert label seriers to ints\n",
        "df = one_hot_encode(df)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    242\n",
            "0    225\n",
            "Name: label, dtype: int64\n",
            "{'beep': 0, 'speech': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vaa4Ch-GpiiM",
        "colab_type": "text"
      },
      "source": [
        "### Train using mfccs_40"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51TgHvZte3R2",
        "colab_type": "code",
        "outputId": "a0786547-0095-4205-c3c1-c92bef4649b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#working model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "features = ['mfccs_40']\n",
        "X, y = generateFeaturesLabels(features)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total number of features 40\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6A80m9C_e8se",
        "colab_type": "code",
        "outputId": "04bfd16c-d32b-407c-9db4-bb053f0c3f21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3131
        }
      },
      "source": [
        "classifiers = train_classifiers(X, y)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***Nearest Neighbors*****\n",
            "Score: 0.8258064516129032\n",
            "cross_val_scores: [0.95727273 0.87022549 0.77751196 0.89145658 0.79802124]\n",
            "Accuracy: 0.86 (+/- 0.13)\n",
            "0.8258064516129032\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.75      0.81        77\n",
            "           1       0.79      0.90      0.84        78\n",
            "\n",
            "   micro avg       0.83      0.83      0.83       155\n",
            "   macro avg       0.83      0.83      0.82       155\n",
            "weighted avg       0.83      0.83      0.82       155\n",
            "\n",
            "[[58 19]\n",
            " [ 8 70]]\n",
            "\n",
            "***Linear SVM*****\n",
            "Score: 0.8451612903225807\n",
            "cross_val_scores: [0.83731395 0.81814043 0.81009009 0.82551595 0.82633053]\n",
            "Accuracy: 0.82 (+/- 0.02)\n",
            "0.8451612903225807\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.77      0.83        77\n",
            "           1       0.80      0.92      0.86        78\n",
            "\n",
            "   micro avg       0.85      0.85      0.85       155\n",
            "   macro avg       0.85      0.84      0.84       155\n",
            "weighted avg       0.85      0.85      0.84       155\n",
            "\n",
            "[[59 18]\n",
            " [ 6 72]]\n",
            "\n",
            "***RBF SVM*****\n",
            "Score: 0.5548387096774193\n",
            "cross_val_scores: [0.51390636 0.45507246 0.47320261 0.51184565 0.41032609]\n",
            "Accuracy: 0.47 (+/- 0.08)\n",
            "0.5548387096774193\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.10      0.19        77\n",
            "           1       0.53      1.00      0.69        78\n",
            "\n",
            "   micro avg       0.55      0.55      0.55       155\n",
            "   macro avg       0.77      0.55      0.44       155\n",
            "weighted avg       0.76      0.55      0.44       155\n",
            "\n",
            "[[ 8 69]\n",
            " [ 0 78]]\n",
            "\n",
            "***Gaussian Process*****\n",
            "Score: 0.8516129032258064\n",
            "cross_val_scores: [0.91454545 0.91427269 0.80370544 0.93529685 0.84803922]\n",
            "Accuracy: 0.88 (+/- 0.10)\n",
            "0.8516129032258064\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.83      0.85        77\n",
            "           1       0.84      0.87      0.86        78\n",
            "\n",
            "   micro avg       0.85      0.85      0.85       155\n",
            "   macro avg       0.85      0.85      0.85       155\n",
            "weighted avg       0.85      0.85      0.85       155\n",
            "\n",
            "[[64 13]\n",
            " [10 68]]\n",
            "\n",
            "***Decision Tree*****\n",
            "Score: 0.8258064516129032\n",
            "cross_val_scores: [0.91485507 0.84040747 0.77751196 0.91372913 0.87059369]\n",
            "Accuracy: 0.86 (+/- 0.10)\n",
            "0.8258064516129032\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.75      0.81        77\n",
            "           1       0.79      0.90      0.84        78\n",
            "\n",
            "   micro avg       0.83      0.83      0.83       155\n",
            "   macro avg       0.83      0.83      0.82       155\n",
            "weighted avg       0.83      0.83      0.82       155\n",
            "\n",
            "[[58 19]\n",
            " [ 8 70]]\n",
            "\n",
            "***Random Forest*****\n",
            "Score: 0.8516129032258064\n",
            "cross_val_scores: [0.92484295 0.87022549 0.83597884 0.87971781 0.83750728]\n",
            "Accuracy: 0.87 (+/- 0.07)\n",
            "0.8516129032258064\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.74      0.83        77\n",
            "           1       0.79      0.96      0.87        78\n",
            "\n",
            "   micro avg       0.85      0.85      0.85       155\n",
            "   macro avg       0.87      0.85      0.85       155\n",
            "weighted avg       0.87      0.85      0.85       155\n",
            "\n",
            "[[57 20]\n",
            " [ 3 75]]\n",
            "\n",
            "***Neural Net*****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Score: 0.8580645161290322\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "cross_val_scores: [0.92484295 0.88232616 0.81162874 0.90282132 0.88150122]\n",
            "Accuracy: 0.88 (+/- 0.08)\n",
            "0.8580645161290322\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.82      0.85        77\n",
            "           1       0.83      0.90      0.86        78\n",
            "\n",
            "   micro avg       0.86      0.86      0.86       155\n",
            "   macro avg       0.86      0.86      0.86       155\n",
            "weighted avg       0.86      0.86      0.86       155\n",
            "\n",
            "[[63 14]\n",
            " [ 8 70]]\n",
            "\n",
            "***AdaBoost*****\n",
            "Score: 0.8580645161290322\n",
            "cross_val_scores: [0.89361702 0.88285941 0.8245283  0.93541667 0.88150122]\n",
            "Accuracy: 0.88 (+/- 0.07)\n",
            "0.8580645161290322\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.79      0.85        77\n",
            "           1       0.82      0.92      0.87        78\n",
            "\n",
            "   micro avg       0.86      0.86      0.86       155\n",
            "   macro avg       0.86      0.86      0.86       155\n",
            "weighted avg       0.86      0.86      0.86       155\n",
            "\n",
            "[[61 16]\n",
            " [ 6 72]]\n",
            "\n",
            "***Naive Bayes*****\n",
            "Score: 0.7935483870967742\n",
            "cross_val_scores: [0.84935897 0.74892579 0.77557915 0.77251019 0.76008443]\n",
            "Accuracy: 0.78 (+/- 0.07)\n",
            "0.7935483870967742\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.64      0.75        77\n",
            "           1       0.73      0.95      0.82        78\n",
            "\n",
            "   micro avg       0.79      0.79      0.79       155\n",
            "   macro avg       0.83      0.79      0.79       155\n",
            "weighted avg       0.82      0.79      0.79       155\n",
            "\n",
            "[[49 28]\n",
            " [ 4 74]]\n",
            "\n",
            "***QDA*****\n",
            "Score: 0.9290322580645162\n",
            "cross_val_scores: [0.92552349 0.90424448 0.89216141 0.94621168 0.92473118]\n",
            "Accuracy: 0.92 (+/- 0.04)\n",
            "0.9290322580645162\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.97      0.93        77\n",
            "           1       0.97      0.88      0.93        78\n",
            "\n",
            "   micro avg       0.93      0.93      0.93       155\n",
            "   macro avg       0.93      0.93      0.93       155\n",
            "weighted avg       0.93      0.93      0.93       155\n",
            "\n",
            "[[75  2]\n",
            " [ 9 69]]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGOtVCzkp4mY",
        "colab_type": "text"
      },
      "source": [
        "### Prediction using mfccs_40"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVtOR89lp80j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "outputId": "a13742ca-bbdd-49a3-b84a-ea2aa1952d33"
      },
      "source": [
        "run_predictions_mfccs(classifiers)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predction using classifier KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
            "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
            "           weights='uniform')\n",
            "36 correct out of 38\n",
            "predction using classifier SVC(C=0.025, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
            "  shrinking=True, tol=0.001, verbose=False)\n",
            "34 correct out of 38\n",
            "predction using classifier SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=2, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "33 correct out of 38\n",
            "predction using classifier GaussianProcessClassifier(copy_X_train=True,\n",
            "             kernel=1**2 * RBF(length_scale=1), max_iter_predict=100,\n",
            "             multi_class='one_vs_rest', n_jobs=None,\n",
            "             n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",
            "             random_state=None, warm_start=False)\n",
            "35 correct out of 38\n",
            "predction using classifier DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "34 correct out of 38\n",
            "predction using classifier RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "            max_depth=5, max_features=1, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
            "            oob_score=False, random_state=None, verbose=0,\n",
            "            warm_start=False)\n",
            "37 correct out of 38\n",
            "predction using classifier MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
            "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
            "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
            "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
            "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
            "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
            "       validation_fraction=0.1, verbose=False, warm_start=False)\n",
            "34 correct out of 38\n",
            "predction using classifier AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
            "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
            "33 correct out of 38\n",
            "predction using classifier GaussianNB(priors=None, var_smoothing=1e-09)\n",
            "32 correct out of 38\n",
            "predction using classifier QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
            "               store_covariance=False, store_covariances=None, tol=0.0001)\n",
            "35 correct out of 38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5xW5SUAfjgS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_model(classifiers[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9q8pAy8_sDY6",
        "colab_type": "text"
      },
      "source": [
        "### Train using multiple features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdTnWbGMsRnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22984545-ea2c-4ffc-834e-bed374f567db"
      },
      "source": [
        "features = ['mfccs_40','chroma', 'mel', 'contrast','tonnetz']\n",
        "X, y = generateFeaturesLabels(features, df)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total number of features 193\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTa7VQGVsYJQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3335
        },
        "outputId": "29922712-6f3c-4f1f-d614-9119cf80faea"
      },
      "source": [
        "multi_feature_classifiers = train_classifiers(X, y)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***Nearest Neighbors*****\n",
            "Score: 0.8129032258064516\n",
            "cross_val_scores: [0.93543956 0.85900542 0.76080833 0.87971781 0.8083404 ]\n",
            "Accuracy: 0.85 (+/- 0.12)\n",
            "0.8129032258064516\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.68      0.78        77\n",
            "           1       0.75      0.95      0.84        78\n",
            "\n",
            "   micro avg       0.81      0.81      0.81       155\n",
            "   macro avg       0.84      0.81      0.81       155\n",
            "weighted avg       0.84      0.81      0.81       155\n",
            "\n",
            "[[52 25]\n",
            " [ 4 74]]\n",
            "\n",
            "***Linear SVM*****\n",
            "Score: 0.9354838709677419\n",
            "cross_val_scores: [0.94651189 0.89284086 0.85857995 0.93487395 0.86015038]\n",
            "Accuracy: 0.90 (+/- 0.07)\n",
            "0.9354838709677419\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94        77\n",
            "           1       0.94      0.94      0.94        78\n",
            "\n",
            "   micro avg       0.94      0.94      0.94       155\n",
            "   macro avg       0.94      0.94      0.94       155\n",
            "weighted avg       0.94      0.94      0.94       155\n",
            "\n",
            "[[72  5]\n",
            " [ 5 73]]\n",
            "\n",
            "***RBF SVM*****\n",
            "Score: 0.5548387096774193\n",
            "cross_val_scores: [0.4949095  0.45507246 0.45294118 0.47320261 0.41032609]\n",
            "Accuracy: 0.46 (+/- 0.06)\n",
            "0.5548387096774193\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.10      0.19        77\n",
            "           1       0.53      1.00      0.69        78\n",
            "\n",
            "   micro avg       0.55      0.55      0.55       155\n",
            "   macro avg       0.77      0.55      0.44       155\n",
            "weighted avg       0.76      0.55      0.44       155\n",
            "\n",
            "[[ 8 69]\n",
            " [ 0 78]]\n",
            "\n",
            "***Gaussian Process*****\n",
            "Score: 0.9161290322580645\n",
            "cross_val_scores: [0.96799455 0.93590909 0.8454416  0.93487395 0.8816657 ]\n",
            "Accuracy: 0.91 (+/- 0.09)\n",
            "0.9161290322580645\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.88      0.91        77\n",
            "           1       0.89      0.95      0.92        78\n",
            "\n",
            "   micro avg       0.92      0.92      0.92       155\n",
            "   macro avg       0.92      0.92      0.92       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "[[68  9]\n",
            " [ 4 74]]\n",
            "\n",
            "***Decision Tree*****\n",
            "Score: 0.8516129032258064\n",
            "cross_val_scores: [0.95736961 0.88232616 0.83750728 0.91388889 0.86015038]\n",
            "Accuracy: 0.89 (+/- 0.08)\n",
            "0.8516129032258064\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.75      0.83        77\n",
            "           1       0.80      0.95      0.87        78\n",
            "\n",
            "   micro avg       0.85      0.85      0.85       155\n",
            "   macro avg       0.87      0.85      0.85       155\n",
            "weighted avg       0.87      0.85      0.85       155\n",
            "\n",
            "[[58 19]\n",
            " [ 4 74]]\n",
            "\n",
            "***Random Forest*****\n",
            "Score: 0.832258064516129\n",
            "cross_val_scores: [0.90424448 0.86156112 0.82551595 0.93511628 0.83750728]\n",
            "Accuracy: 0.87 (+/- 0.08)\n",
            "0.832258064516129\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.82      0.83        77\n",
            "           1       0.82      0.85      0.84        78\n",
            "\n",
            "   micro avg       0.83      0.83      0.83       155\n",
            "   macro avg       0.83      0.83      0.83       155\n",
            "weighted avg       0.83      0.83      0.83       155\n",
            "\n",
            "[[63 14]\n",
            " [12 66]]\n",
            "\n",
            "***Neural Net*****\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Score: 0.9032258064516129\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "cross_val_scores: [0.92484295 0.82696733 0.83379006 0.89094747 0.88150122]\n",
            "Accuracy: 0.87 (+/- 0.07)\n",
            "0.9032258064516129\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.88      0.90        77\n",
            "           1       0.89      0.92      0.91        78\n",
            "\n",
            "   micro avg       0.90      0.90      0.90       155\n",
            "   macro avg       0.90      0.90      0.90       155\n",
            "weighted avg       0.90      0.90      0.90       155\n",
            "\n",
            "[[68  9]\n",
            " [ 6 72]]\n",
            "\n",
            "***AdaBoost*****\n",
            "Score: 0.8903225806451613\n",
            "cross_val_scores: [0.96805257 0.93605442 0.84732645 0.96768215 0.91396855]\n",
            "Accuracy: 0.93 (+/- 0.09)\n",
            "0.8903225806451613\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.82      0.88        77\n",
            "           1       0.84      0.96      0.90        78\n",
            "\n",
            "   micro avg       0.89      0.89      0.89       155\n",
            "   macro avg       0.90      0.89      0.89       155\n",
            "weighted avg       0.90      0.89      0.89       155\n",
            "\n",
            "[[63 14]\n",
            " [ 3 75]]\n",
            "\n",
            "***Naive Bayes*****\n",
            "Score: 0.8387096774193549\n",
            "cross_val_scores: [0.87022549 0.81144543 0.77339181 0.86839623 0.81410935]\n",
            "Accuracy: 0.83 (+/- 0.07)\n",
            "0.8387096774193549\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.70      0.81        77\n",
            "           1       0.77      0.97      0.86        78\n",
            "\n",
            "   micro avg       0.84      0.84      0.84       155\n",
            "   macro avg       0.87      0.84      0.84       155\n",
            "weighted avg       0.87      0.84      0.84       155\n",
            "\n",
            "[[54 23]\n",
            " [ 2 76]]\n",
            "\n",
            "***QDA*****\n",
            "Score: 0.7935483870967742\n",
            "cross_val_scores: [0.74656863 0.64812185 0.59913793 0.45294118 0.41032609]\n",
            "Accuracy: 0.57 (+/- 0.25)\n",
            "0.7935483870967742\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.97      0.82        77\n",
            "           1       0.96      0.62      0.75        78\n",
            "\n",
            "   micro avg       0.79      0.79      0.79       155\n",
            "   macro avg       0.84      0.79      0.79       155\n",
            "weighted avg       0.84      0.79      0.79       155\n",
            "\n",
            "[[75  2]\n",
            " [30 48]]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzYAeNJ9uazA",
        "colab_type": "text"
      },
      "source": [
        "### Test using Mutliple Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIZ6aSBWufPy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "outputId": "ab9cc297-573c-4dc0-f1ca-017dd961f417"
      },
      "source": [
        "run_multi_features_predictions(multi_feature_classifiers)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predction using classifier KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
            "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
            "           weights='uniform')\n",
            "36 correct out of 38\n",
            "predction using classifier SVC(C=0.025, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
            "  shrinking=True, tol=0.001, verbose=False)\n",
            "19 correct out of 38\n",
            "predction using classifier SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=2, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "29 correct out of 38\n",
            "predction using classifier GaussianProcessClassifier(copy_X_train=True,\n",
            "             kernel=1**2 * RBF(length_scale=1), max_iter_predict=100,\n",
            "             multi_class='one_vs_rest', n_jobs=None,\n",
            "             n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",
            "             random_state=None, warm_start=False)\n",
            "32 correct out of 38\n",
            "predction using classifier DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
            "            max_features=None, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
            "            splitter='best')\n",
            "29 correct out of 38\n",
            "predction using classifier RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "            max_depth=5, max_features=1, max_leaf_nodes=None,\n",
            "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "            min_samples_leaf=1, min_samples_split=2,\n",
            "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
            "            oob_score=False, random_state=None, verbose=0,\n",
            "            warm_start=False)\n",
            "31 correct out of 38\n",
            "predction using classifier MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
            "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
            "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
            "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
            "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
            "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
            "       validation_fraction=0.1, verbose=False, warm_start=False)\n",
            "2 correct out of 38\n",
            "predction using classifier AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
            "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
            "31 correct out of 38\n",
            "predction using classifier GaussianNB(priors=None, var_smoothing=1e-09)\n",
            "33 correct out of 38\n",
            "predction using classifier QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
            "               store_covariance=False, store_covariances=None, tol=0.0001)\n",
            "13 correct out of 38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-k_r569wPlS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "save_model(multi_feature_classifiers[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7d0XlOLtzsQi",
        "colab_type": "text"
      },
      "source": [
        "### Tests with only using augmented files in training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3CBy1bPsqQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = df[df['augmented'] == True];"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxVDu4_dZ20C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#convert label seriers to ints\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "df['label'] = le.fit_transform(df['label'].astype(str))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYm9CcqnezM9",
        "colab_type": "code",
        "outputId": "18e7171d-2895-4153-95ae-caf3ec477dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(df.label.value_counts())\n",
        "le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
        "print(le_name_mapping)\n",
        "class_names = le.classes_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    242\n",
            "0    225\n",
            "Name: label, dtype: int64\n",
            "{'beep': 0, 'speech': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7TTe0_Vut3u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "augmented_sort = df.sort_values(by=['augmented'], ascending=False)\n",
        "augmented_sort[:len(t)]\n",
        "train = augmented_sort[:len(t)]\n",
        "test = augmented_sort[len(t):]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xd8r11SkxewY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = list(train['mfccs_40'].values)\n",
        "y_train = train['label'].values\n",
        "\n",
        "X_test = list(test['mfccs_40'].values)\n",
        "y_test = test['label'].values\n",
        "\n",
        "# X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EaSpo72qyb-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code source: Gaël Varoquaux\n",
        "#              Andreas Müller\n",
        "# Modified for documentation by Jaques Grobler\n",
        "# License: BSD 3 clause\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.datasets import make_moons, make_circles, make_classification\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "\n",
        "trained_classifiers = []\n",
        "classifiers = [\n",
        "    KNeighborsClassifier(3),\n",
        "    SVC(kernel=\"linear\", C=0.025),\n",
        "    SVC(gamma=2, C=1),\n",
        "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
        "    DecisionTreeClassifier(max_depth=5),\n",
        "    RandomForestClassifier(max_depth=10, n_estimators=10, max_features=1),\n",
        "    MLPClassifier(alpha=1),\n",
        "    AdaBoostClassifier(),\n",
        "    GaussianNB(),\n",
        "    QuadraticDiscriminantAnalysis()]\n",
        "\n",
        "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
        "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
        "         \"Naive Bayes\", \"QDA\"]\n",
        "i = 0\n",
        "for classifier in classifiers:\n",
        "  print(\"***\"+ names[i] + \"*****\")\n",
        "  \n",
        "  fit_model = classifier.fit(X_train, y_train)\n",
        "  trained_classifiers.append(fit_model)\n",
        "\n",
        "  print(\"Score:\",fit_model.score(X_test, y_test))\n",
        "\n",
        "  cross_val_scores = cross_val_score(fit_model, X, y, cv=5, scoring='f1_macro')\n",
        "  print(\"cross_val_scores:\", cross_val_scores)\n",
        "  print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cross_val_scores.mean(), cross_val_scores.std() * 2))\n",
        "\n",
        "  predictions = fit_model.predict(X_test)\n",
        "\n",
        "  print(metrics.accuracy_score(y_test, predictions))\n",
        "  print(metrics.classification_report(y_test, predictions))\n",
        "  print(metrics.confusion_matrix(y_test, predictions))\n",
        "  print(\"\")\n",
        "  i = i + 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}